subject:(Activity recognition)

University of Texas – Austin

1. Chen, Chao-Yeh. Learning human activities and poses with interconnected data sources.

Degree: PhD, Computer science, 2016, University of Texas – Austin

► Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such… (more)

▼ Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such as action recognition, pose estimation, human-object interaction, and activity detection. Knowledge of actions and poses could benefit many applications, including video search, surveillance, auto-tagging, event detection, and human-computer interfaces. To understand humans' actions and poses, we need to address several challenges. First, humans are able to perform an enormous amount of poses. For example, simply to move forward, we can do crawling, walking, running, and sprinting. These poses all look different and require examples to cover these variations. Second, the appearance of a person's pose changes when looking from different viewing angles. The learned action model needs to cover the variations from different views. Third, many actions involve interactions between people and other objects, so we need to consider the appearance change corresponding to that object as well. Fourth, collecting such data for learning is difficult and expensive. Last, even if we can learn a good model for an action, to localize when and where the action happens in a long video remains a difficult problem due to the large search space. My key idea to alleviate these obstacles in learning humans' actions and poses is to discover the underlying patterns that connect the information from different data sources. Why will there be underlying patterns? The intuition is that all people share the same articulated physical structure. Though we can change our pose, there are common regulations that limit how our pose can be and how it can move over time. Therefore, all types of human data will follow these rules and they can serve as prior knowledge or regularization in our learning framework. If we can exploit these tendencies, we are able to extract additional information from data and use them to improve learning of humans' actions and poses. In particular, we are able to find patterns for how our pose could vary over time, how our appearance looks in a specific view, how our pose is when we are interacting with objects with certain properties, and how part of our body configuration is shared across different poses. If we could learn these patterns, they can be used to interconnect and extrapolate the knowledge between different data sources. To this end, I propose several new ways to connect human activity data. First, I show how to connect snapshot images and videos by exploring the patterns of how our pose could change over time. Building on this idea, I explore how to connect humans' poses across multiple views by discovering the correlations between different poses and the latent factors that affect the viewpoint variations. In addition, I consider if there are also patterns connecting our poses and nearby objects when we are interacting with them. Furthermore, I explore how we can utilize the predicted interaction as a cue to better address existing recognition problems… Advisors/Committee Members: Grauman, Kristen Lorraine, 1979- (advisor), Aggarwal, Jake K. (committee member), Mooney, Raymond J. (committee member), Ramanan, Deva (committee member), Stone, Peter (committee member).

Subjects/Keywords: Activity recognition; Activity detection

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APA (6^th Edition):

Chen, C. (2016). Learning human activities and poses with interconnected data sources. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/40260

Chicago Manual of Style (16^th Edition):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed December 15, 2019. http://hdl.handle.net/2152/40260.

MLA Handbook (7^th Edition):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Web. 15 Dec 2019.

Vancouver:

Chen C. Learning human activities and poses with interconnected data sources. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2152/40260.

Council of Science Editors:

Chen C. Learning human activities and poses with interconnected data sources. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/40260

University of Illinois – Urbana-Champaign

2. Arora, Rohan R. Metrics for analytics and visualization of big data with applications to activity recognition.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/90953

► Activity recognition systems detect the hidden actions of an agent from sensor measurements made on the agents' actions and the environmental conditions. For such systems,… (more)

Subjects/Keywords: activity; recognition; metrics

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APA (6^th Edition):

Arora, R. R. (2016). Metrics for analytics and visualization of big data with applications to activity recognition. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90953

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Arora, Rohan R. “Metrics for analytics and visualization of big data with applications to activity recognition.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed December 15, 2019. http://hdl.handle.net/2142/90953.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Arora, Rohan R. “Metrics for analytics and visualization of big data with applications to activity recognition.” 2016. Web. 15 Dec 2019.

Vancouver:

Arora RR. Metrics for analytics and visualization of big data with applications to activity recognition. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2142/90953.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Arora RR. Metrics for analytics and visualization of big data with applications to activity recognition. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90953

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

3. Ahsan, Unaiza. Leveraging Mid-level Representations for Complex Activity Recognition.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/61199

► Dynamic scene understanding requires learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition from images and videos… (more)

▼ Dynamic scene understanding requires learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition from images and videos requires annotating large datasets with action labels which is a tedious and expensive task. Thus, there is a need to design a mid-level or intermediate feature representation which does not require millions of labels, yet is able to generalize to semantic-level recognition of activities in visual data. This thesis makes three contributions in this regard. First, we propose an event concept-based intermediate representation which learns concepts via the Web and uses this representation to identify events even with a single labeled example. To demonstrate the strength of the proposed approaches, we contribute two diverse social event datasets to the community. We then present a use case of event concepts as a mid-level representation that generalizes to sentiment recognition in diverse social event images. Second, we propose to train Generative Adversarial Networks (GANs) with video frames (which does not require labels), use the trained discriminator from GANs as an intermediate representation and finetune it on a smaller labeled video activity dataset to recognize actions in videos. This unsupervised pre-training step avoids any manual feature engineering, video frame encoding or searching for the best video frame sampling technique. Our third contribution is a self-supervised learning approach on videos that exploits both spatial and temporal coherency to learn feature representations on video data without any supervision. We demonstrate the transfer learning capability of this model on smaller labeled datasets. We present comprehensive experimental analysis on the self-supervised model to provide insights into the unsupervised pretraining paradigm and how it can help with activity recognition on target datasets which the model has never seen during training. Advisors/Committee Members: Essa, Irfan (advisor), Hays, James (committee member), De Choudhury, Munmun (committee member), Kira, Zsolt (committee member), Parikh, Devi (committee member), Sun, Chen (committee member).

Subjects/Keywords: activity recognition; self-supervised learning; event recognition

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APA (6^th Edition):

Ahsan, U. (2019). Leveraging Mid-level Representations for Complex Activity Recognition. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61199

Chicago Manual of Style (16^th Edition):

Ahsan, Unaiza. “Leveraging Mid-level Representations for Complex Activity Recognition.” 2019. Doctoral Dissertation, Georgia Tech. Accessed December 15, 2019. http://hdl.handle.net/1853/61199.

MLA Handbook (7^th Edition):

Ahsan, Unaiza. “Leveraging Mid-level Representations for Complex Activity Recognition.” 2019. Web. 15 Dec 2019.

Vancouver:

Ahsan U. Leveraging Mid-level Representations for Complex Activity Recognition. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1853/61199.

Council of Science Editors:

Ahsan U. Leveraging Mid-level Representations for Complex Activity Recognition. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61199

Rochester Institute of Technology

4. Dudley, Michael David. Recognition of human interactions using limb-level feature points.

Degree: Computer Engineering, 2009, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/3223

► Human activity recognition is an emerging area of research in computer vision with applications in video surveillance, human-computer interaction, robotics, and video annotation. Despite a… (more)

Subjects/Keywords: Activity recognition; Computer vision; Interaction recognition

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APA (6^th Edition):

Dudley, M. D. (2009). Recognition of human interactions using limb-level feature points. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/3223

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Dudley, Michael David. “Recognition of human interactions using limb-level feature points.” 2009. Thesis, Rochester Institute of Technology. Accessed December 15, 2019. https://scholarworks.rit.edu/theses/3223.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dudley, Michael David. “Recognition of human interactions using limb-level feature points.” 2009. Web. 15 Dec 2019.

Vancouver:

Dudley MD. Recognition of human interactions using limb-level feature points. [Internet] [Thesis]. Rochester Institute of Technology; 2009. [cited 2019 Dec 15]. Available from: https://scholarworks.rit.edu/theses/3223.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dudley MD. Recognition of human interactions using limb-level feature points. [Thesis]. Rochester Institute of Technology; 2009. Available from: https://scholarworks.rit.edu/theses/3223

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Wright State University

5. Angeleas, Anargyros. A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction.

Degree: PhD, Computer Science and Engineering PhD, 2018, Wright State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238

► Human Activity Recognition is an actively researched domain for the past fewdecades, and is one of the most eminent applications of today. It is already… (more)

Subjects/Keywords: Computer Science; Human Activity Recognition

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APA (6^th Edition):

Angeleas, A. (2018). A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238

Chicago Manual of Style (16^th Edition):

Angeleas, Anargyros. “A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction.” 2018. Doctoral Dissertation, Wright State University. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238.

MLA Handbook (7^th Edition):

Angeleas, Anargyros. “A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction.” 2018. Web. 15 Dec 2019.

Vancouver:

Angeleas A. A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction. [Internet] [Doctoral dissertation]. Wright State University; 2018. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238.

Council of Science Editors:

Angeleas A. A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction. [Doctoral Dissertation]. Wright State University; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238

University of Edinburgh

6. Vafeias, Efstathios. Recognising activities by jointly modelling actions and their effects.

Degree: PhD, 2015, University of Edinburgh

URL: http://hdl.handle.net/1842/14182

► With the rapid increase in adoption of consumer technologies, including inexpensive but powerful hardware, robotics appears poised at the cusp of widespread deployment in human… (more)

Subjects/Keywords: 006.3; robotic vision; activity recognition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vafeias, E. (2015). Recognising activities by jointly modelling actions and their effects. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/14182

Chicago Manual of Style (16^th Edition):

Vafeias, Efstathios. “Recognising activities by jointly modelling actions and their effects.” 2015. Doctoral Dissertation, University of Edinburgh. Accessed December 15, 2019. http://hdl.handle.net/1842/14182.

MLA Handbook (7^th Edition):

Vafeias, Efstathios. “Recognising activities by jointly modelling actions and their effects.” 2015. Web. 15 Dec 2019.

Vancouver:

Vafeias E. Recognising activities by jointly modelling actions and their effects. [Internet] [Doctoral dissertation]. University of Edinburgh; 2015. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1842/14182.

Council of Science Editors:

Vafeias E. Recognising activities by jointly modelling actions and their effects. [Doctoral Dissertation]. University of Edinburgh; 2015. Available from: http://hdl.handle.net/1842/14182

Hong Kong University of Science and Technology

7. Hu, Hao. Learning-based human activity recognition.

Degree: 2012, Hong Kong University of Science and Technology

URL: https://doi.org/10.14711/thesis-b1206070 ; http://repository.ust.hk/ir/bitstream/1783.1-7811/1/th_redirect.html

► Recognizing human activities has been an extensive and interesting research topic since early 1980s. However, when deploying human activity recognition solutions to the real world,… (more)

▼ Recognizing human activities has been an extensive and interesting research topic since early 1980s. However, when deploying human activity recognition solutions to the real world, the solutions we provide must satisfy a series of requirements. We would expect our solution to be able to learn a reasonable model from as limited training data as possible. We also hope our solution would be able to deal with the complex relationships which exist in human activities. As is the case for almost all machine learning solutions, we would hope that our solution is scalable and efficient. In this thesis, we start by surveying related work and then study the solution to some specific challenges which are important to deploy these activity recognition systems in the real world. Specifically,We first analyze how to recognize multiple activities in the physical world environment, especially when such activities have concurrent and interleaving relationships. Next, we extend such a framework to the problem of Web query classification, by exploiting the relatedness of search queries to activities with interleaving relationships and propose a context-aware query classification algorithm. Secondly, we study the problem of abnormal activity recognition. These abnormal activities are rare to happen and it is difficult to collect enough training data about them. We design an algorithm based on the Hierarchical Dirichlet Process and the one-class Support Vector Machine to recognize abnormal activities when the training data is scarce. Finally, when we need to deploy the activity recognition systems in the real-world, it is impractical for us to collect enough training data for different activity recognition scenarios, especially when we need to collect training data for different persons and even for different actions. To solve this problem, we’ve developed an activity recognition framework based on transfer learning which borrows useful information from previously collected and learned activity recognition domains and then re-use such information into the new target activity recognition domain. Furthermore, we’ve conducted extensive experiments to demonstrate the effectiveness of our proposed approaches on real-world datasets collected from smart homes or sensor environments. We’ve also shown that our context-aware query classification algorithm could outperform state-of-the-art query classification approaches on real-world query engine search logs. At the end of this thesis, we discuss some possible directions and problems for future work and extensions.

Subjects/Keywords: Human activity recognition; Machine learning

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APA (6^th Edition):

Hu, H. (2012). Learning-based human activity recognition. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1206070 ; http://repository.ust.hk/ir/bitstream/1783.1-7811/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hu, Hao. “Learning-based human activity recognition.” 2012. Thesis, Hong Kong University of Science and Technology. Accessed December 15, 2019. https://doi.org/10.14711/thesis-b1206070 ; http://repository.ust.hk/ir/bitstream/1783.1-7811/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hu, Hao. “Learning-based human activity recognition.” 2012. Web. 15 Dec 2019.

Vancouver:

Hu H. Learning-based human activity recognition. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2012. [cited 2019 Dec 15]. Available from: https://doi.org/10.14711/thesis-b1206070 ; http://repository.ust.hk/ir/bitstream/1783.1-7811/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hu H. Learning-based human activity recognition. [Thesis]. Hong Kong University of Science and Technology; 2012. Available from: https://doi.org/10.14711/thesis-b1206070 ; http://repository.ust.hk/ir/bitstream/1783.1-7811/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Western Carolina University

8. Shouse, Kirke. Activity recognition using Grey-Markov model.

Degree: 2011, Western Carolina University

URL: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=9032

► Activity Recognition (AR) is a process of identifying actions and goals of one or more agents of interest. AR techniques have been applied to both… (more)

Subjects/Keywords: Human activity recognition; Markov processes

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APA (6^th Edition):

Shouse, K. (2011). Activity recognition using Grey-Markov model. (Masters Thesis). Western Carolina University. Retrieved from http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=9032

Chicago Manual of Style (16^th Edition):

Shouse, Kirke. “Activity recognition using Grey-Markov model.” 2011. Masters Thesis, Western Carolina University. Accessed December 15, 2019. http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=9032.

MLA Handbook (7^th Edition):

Shouse, Kirke. “Activity recognition using Grey-Markov model.” 2011. Web. 15 Dec 2019.

Vancouver:

Shouse K. Activity recognition using Grey-Markov model. [Internet] [Masters thesis]. Western Carolina University; 2011. [cited 2019 Dec 15]. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=9032.

Council of Science Editors:

Shouse K. Activity recognition using Grey-Markov model. [Masters Thesis]. Western Carolina University; 2011. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=9032

University of Melbourne

9. Li, Han. Low-cost leaving home activity recognition using mobile sensing.

Degree: 2017, University of Melbourne

URL: http://hdl.handle.net/11343/129509

► Leaving home activity recognition (LHAR) is essential in context-aware applications. For example, on a rainy day, a smartphone can remind a user to bring an… (more)

▼ Leaving home activity recognition (LHAR) is essential in context-aware applications. For example, on a rainy day, a smartphone can remind a user to bring an umbrella when the leaving home activity is recognized. However, research in this field is substantially lacking. Most existing studies require extra hardware such as sensors installed at home to help recognize such activities, which limits the applicability of these studies. With the ubiquity of mobile sensing technique, it becomes feasible for a smartphone to sense the ambient environment and a user’s current context. In this thesis, we develop a low-cost system using mobile sensing for timely recognition of leaving home activities. To the best of our knowledge, we are the first to recognize leaving home activities using only sensors on smartphones. Overall, our system can recognize leaving home activities within 20 seconds after the home door is closed with a precision of 93.1% and recall of 100%. Recognizing leaving home activities while only leveraging sensors on smartphones can be challenging in two aspects: 1) the diversity of home environments result in inconsistent features, which significantly affects the recognition performance; and 2) mobile sensing is restricted by limited resources on smartphones, e.g., power and computation capability. To overcome such limitations, we first investigate sensors available on commodity smartphones and find that features extracted from WiFi, barometer, cell tower, magnetic field sensor and accelerometer readings are relatively robust in recognizing leaving home activities. Second, due to the variety of residential property environments, we propose a sensor selection algorithm to adaptively select the most suitable sensors for each home to personalize the training. Both classification performance and sensing cost are taken into consideration to provide the user an option to trade power consumption for recognition accuracy, and vice versa. Inspired by the observation that leaving home activity usually involves walking, we activate the power-hungry sensors to start the recognition only when the walking event is detected. Thus, we reduce the sensing cost by 76.65%.

Subjects/Keywords: mobile sensing; activity recognition; leaving home activity recognition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, H. (2017). Low-cost leaving home activity recognition using mobile sensing. (Masters Thesis). University of Melbourne. Retrieved from http://hdl.handle.net/11343/129509

Chicago Manual of Style (16^th Edition):

Li, Han. “Low-cost leaving home activity recognition using mobile sensing.” 2017. Masters Thesis, University of Melbourne. Accessed December 15, 2019. http://hdl.handle.net/11343/129509.

MLA Handbook (7^th Edition):

Li, Han. “Low-cost leaving home activity recognition using mobile sensing.” 2017. Web. 15 Dec 2019.

Vancouver:

Li H. Low-cost leaving home activity recognition using mobile sensing. [Internet] [Masters thesis]. University of Melbourne; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/11343/129509.

Council of Science Editors:

Li H. Low-cost leaving home activity recognition using mobile sensing. [Masters Thesis]. University of Melbourne; 2017. Available from: http://hdl.handle.net/11343/129509

University of North Texas

10. Janmohammadi, Siamak. Classifying Pairwise Object Interactions: A Trajectory Analytics Approach.

Degree: 2015, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc801901/

► We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object… (more)

Subjects/Keywords: action recognition; machine learning; trajectory analysis; supervised classification methods; activity recognition; Human activity recognition.; Pattern recognition systems.; Machine learning.; Electronic surveillance.

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APA (6^th Edition):

Janmohammadi, S. (2015). Classifying Pairwise Object Interactions: A Trajectory Analytics Approach. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc801901/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Janmohammadi, Siamak. “Classifying Pairwise Object Interactions: A Trajectory Analytics Approach.” 2015. Thesis, University of North Texas. Accessed December 15, 2019. https://digital.library.unt.edu/ark:/67531/metadc801901/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Janmohammadi, Siamak. “Classifying Pairwise Object Interactions: A Trajectory Analytics Approach.” 2015. Web. 15 Dec 2019.

Vancouver:

Janmohammadi S. Classifying Pairwise Object Interactions: A Trajectory Analytics Approach. [Internet] [Thesis]. University of North Texas; 2015. [cited 2019 Dec 15]. Available from: https://digital.library.unt.edu/ark:/67531/metadc801901/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Janmohammadi S. Classifying Pairwise Object Interactions: A Trajectory Analytics Approach. [Thesis]. University of North Texas; 2015. Available from: https://digital.library.unt.edu/ark:/67531/metadc801901/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Texas

11. Santiteerakul, Wasana. Trajectory Analytics.

Degree: 2015, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc801885/

► The numerous surveillance videos recorded by a single stationary wide-angle-view camera persuade the use of a moving point as the representation of each small-size object… (more)

Subjects/Keywords: trajectory analytics; action recognition; activity recognition; Pattern recognition systems.; Machine learning.; Human activity recognition.; Electronic surveillance.

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APA (6^th Edition):

Santiteerakul, W. (2015). Trajectory Analytics. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc801885/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Santiteerakul, Wasana. “Trajectory Analytics.” 2015. Thesis, University of North Texas. Accessed December 15, 2019. https://digital.library.unt.edu/ark:/67531/metadc801885/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Santiteerakul, Wasana. “Trajectory Analytics.” 2015. Web. 15 Dec 2019.

Vancouver:

Santiteerakul W. Trajectory Analytics. [Internet] [Thesis]. University of North Texas; 2015. [cited 2019 Dec 15]. Available from: https://digital.library.unt.edu/ark:/67531/metadc801885/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Santiteerakul W. Trajectory Analytics. [Thesis]. University of North Texas; 2015. Available from: https://digital.library.unt.edu/ark:/67531/metadc801885/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Massey University

12. Ranhotigmage, Chagitha. Human activities & posture recognition : innovative algorithm for highly accurate detection rate : a thesis submitted in fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Systems Engineering at Massey University, Palmerston North, New Zealand .

Degree: 2013, Massey University

URL: http://hdl.handle.net/10179/4339

► The main purpose of thesis is to introduce new innovative algorithm for “unintentional fall detection” with 100% accuracy of detecting falls on hard surfaces which… (more)

Subjects/Keywords: Human activity recognition; Mathematical models; Algorithm

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APA (6^th Edition):

Ranhotigmage, C. (2013). Human activities & posture recognition : innovative algorithm for highly accurate detection rate : a thesis submitted in fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Systems Engineering at Massey University, Palmerston North, New Zealand . (Thesis). Massey University. Retrieved from http://hdl.handle.net/10179/4339

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ranhotigmage, Chagitha. “Human activities & posture recognition : innovative algorithm for highly accurate detection rate : a thesis submitted in fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Systems Engineering at Massey University, Palmerston North, New Zealand .” 2013. Thesis, Massey University. Accessed December 15, 2019. http://hdl.handle.net/10179/4339.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ranhotigmage, Chagitha. “Human activities & posture recognition : innovative algorithm for highly accurate detection rate : a thesis submitted in fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Systems Engineering at Massey University, Palmerston North, New Zealand .” 2013. Web. 15 Dec 2019.

Vancouver:

Ranhotigmage C. Human activities & posture recognition : innovative algorithm for highly accurate detection rate : a thesis submitted in fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Systems Engineering at Massey University, Palmerston North, New Zealand . [Internet] [Thesis]. Massey University; 2013. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10179/4339.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ranhotigmage C. Human activities & posture recognition : innovative algorithm for highly accurate detection rate : a thesis submitted in fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Systems Engineering at Massey University, Palmerston North, New Zealand . [Thesis]. Massey University; 2013. Available from: http://hdl.handle.net/10179/4339

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oklahoma State University

13. Kallur, Dharmendra Chandrashekar. Human Localization and Activity Recognition Using Distributed Motion Sensors.

Degree: Electrical Engineering, 2014, Oklahoma State University

URL: http://hdl.handle.net/11244/14924

► The purpose of this thesis is to localize a human and recognize his/her activities in indoor environments using distributed motion sensors. We propose to use… (more)

▼ The purpose of this thesis is to localize a human and recognize his/her activities in indoor environments using distributed motion sensors. We propose to use a test bed simulated as mock apartment for conducting our experiments. The two parts of the thesis are localization and activity recognition of the elderly person. We explain complete hardware and software setup used to provide these services. The hardware setup consists of two types of sensor end nodes and two sink nodes. The two types of end nodes are Passive Infrared sensor node and GridEye sensor node. Passive Infrared sensor nodes consist of Passive Infrared sensors for motion detection. GridEye sensor nodes consist of thermal array sensors. Data from these sensors are acquired using Arduino boards and transmitted using Xbee modules to the sink nodes. The sink nodes consist of receiver Xbee modules connected to a computer. The sensor nodes were strategically placed at different place inside the apartment. The thermal array sensor provides 64 pixel temperature values, while the PIR sensor provides binary information about motion in its field of view. Since the thermal array sensor provides more information, they were placed in large rooms such as living room and bed room. While PIR sensors were placed in kitchen and bathroom. Initially GridEye sensors are calibrated to obtain the transformation between pixel and real world coordinates. Data from these sensors were processed on computer and we were able to localize the human inside the apartment. We compared the location accuracy using ground truth data obtained from the OptiTrack system. GridEye sensors were also used for activity recognition. Basic human activities such as sitting, sleeping, standing and walking were recognized. We used Support Vector Machine (SVM) to recognize sitting and sleeping activities. Gait speed of human was used to recognize the standing and walking activities. Experiments were performed to obtain the accuracy of classification for these activities. Advisors/Committee Members: Sheng, Weihua (advisor), Cheng, Qi (committee member), Ramakumar, Rama (committee member).

Subjects/Keywords: activity recognition; home automation; indoor human localization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kallur, D. C. (2014). Human Localization and Activity Recognition Using Distributed Motion Sensors. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/14924

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kallur, Dharmendra Chandrashekar. “Human Localization and Activity Recognition Using Distributed Motion Sensors.” 2014. Thesis, Oklahoma State University. Accessed December 15, 2019. http://hdl.handle.net/11244/14924.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kallur, Dharmendra Chandrashekar. “Human Localization and Activity Recognition Using Distributed Motion Sensors.” 2014. Web. 15 Dec 2019.

Vancouver:

Kallur DC. Human Localization and Activity Recognition Using Distributed Motion Sensors. [Internet] [Thesis]. Oklahoma State University; 2014. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/11244/14924.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kallur DC. Human Localization and Activity Recognition Using Distributed Motion Sensors. [Thesis]. Oklahoma State University; 2014. Available from: http://hdl.handle.net/11244/14924

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oklahoma State University

14. Li, Gang. ASCCbot: An Open Mobile Robot Platform.

Degree: School of Electrical & Computer Engineering, 2011, Oklahoma State University

URL: http://hdl.handle.net/11244/10237

► ASCCbot, an open mobile platform built in ASCC lab, is presented in this thesis. The hardware and software design of the ASCCbot makes it a… (more)

Subjects/Keywords: activity recognition; mobile robot; semantic mapping; telepresence

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APA (6^th Edition):

Li, G. (2011). ASCCbot: An Open Mobile Robot Platform. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/10237

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Gang. “ASCCbot: An Open Mobile Robot Platform.” 2011. Thesis, Oklahoma State University. Accessed December 15, 2019. http://hdl.handle.net/11244/10237.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Gang. “ASCCbot: An Open Mobile Robot Platform.” 2011. Web. 15 Dec 2019.

Vancouver:

Li G. ASCCbot: An Open Mobile Robot Platform. [Internet] [Thesis]. Oklahoma State University; 2011. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/11244/10237.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li G. ASCCbot: An Open Mobile Robot Platform. [Thesis]. Oklahoma State University; 2011. Available from: http://hdl.handle.net/11244/10237

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Robert Gordon University

15. Bashir, Sulaimon A. Change detection for activity recognition.

Degree: PhD, 2017, Robert Gordon University

URL: http://hdl.handle.net/10059/3104

► Activity Recognition is concerned with identifying the physical state of a user at a particular point in time. Activity recognition task requires the training of… (more)

▼ Activity Recognition is concerned with identifying the physical state of a user at a particular point in time. Activity recognition task requires the training of classification algorithm using the processed sensor data from the representative population of users. The accuracy of the generated model often reduces during classification of new instances due to the non-stationary sensor data and variations in user characteristics. Thus, there is a need to adapt the classification model to new user haracteristics. However, the existing approaches to model adaptation in activity recognition are blind. They continuously adapt a classification model at a regular interval without specific and precise detection of the indicator of the degrading performance of the model. This approach can lead to wastage of system resources dedicated to continuous adaptation. This thesis addresses the problem of detecting changes in the accuracy of activity recognition model. The thesis developed a classifier for activity recognition. The classifier uses three statistical summaries data that can be generated from any dataset for similarity based classification of new samples. The weighted ensemble combination of the classification decision from each statistical summary data results in a better performance than three existing benchmarked classification algorithms. The thesis also presents change detection approaches that can detect the changes in the accuracy of the underlying recognition model without having access to the ground truth label of each activity being recognised. The first approach called `UDetect' computes the change statistics from the window of classified data and employed statistical process control method to detect variations between the classified data and the reference data of a class. Evaluation of the approach indicates a consistent detection that correlates with the error rate of the model. The second approach is a distance based change detection technique that relies on the developed statistical summaries data for comparing new classified samples and detects any drift in the original class of the activity. The implemented approach uses distance function and a threshold parameter to detect the accuracy change in the classifier that is classifying new instances. Evaluation of the approach yields above 90% detection accuracy. Finally, a layered framework for activity recognition is proposed to make model adaptation in activity recognition informed using the developed techniques in this thesis.

Subjects/Keywords: Activity recognition; Change detection; Classification algorithm; Sensors

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APA (6^th Edition):

Bashir, S. A. (2017). Change detection for activity recognition. (Doctoral Dissertation). Robert Gordon University. Retrieved from http://hdl.handle.net/10059/3104

Chicago Manual of Style (16^th Edition):

Bashir, Sulaimon A. “Change detection for activity recognition.” 2017. Doctoral Dissertation, Robert Gordon University. Accessed December 15, 2019. http://hdl.handle.net/10059/3104.

MLA Handbook (7^th Edition):

Bashir, Sulaimon A. “Change detection for activity recognition.” 2017. Web. 15 Dec 2019.

Vancouver:

Bashir SA. Change detection for activity recognition. [Internet] [Doctoral dissertation]. Robert Gordon University; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10059/3104.

Council of Science Editors:

Bashir SA. Change detection for activity recognition. [Doctoral Dissertation]. Robert Gordon University; 2017. Available from: http://hdl.handle.net/10059/3104

University of Houston

16. Sharma, Sarthak 1994-. Device Free Activity Recognition using Ultra-Wideband Radio Communication.

Degree: Computer Science, Department of, 2018, University of Houston

URL: http://hdl.handle.net/10657/3303

► Human Activity Recognition (HAR) is a fundamental building block in many Internet of Things (IoT) applications. Although there has been a lot of interest in… (more)

Subjects/Keywords: Ultra-Wideband; Device Free; activity recognition

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APA (6^th Edition):

Sharma, S. 1. (2018). Device Free Activity Recognition using Ultra-Wideband Radio Communication. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3303

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sharma, Sarthak 1994-. “Device Free Activity Recognition using Ultra-Wideband Radio Communication.” 2018. Thesis, University of Houston. Accessed December 15, 2019. http://hdl.handle.net/10657/3303.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sharma, Sarthak 1994-. “Device Free Activity Recognition using Ultra-Wideband Radio Communication.” 2018. Web. 15 Dec 2019.

Vancouver:

Sharma S1. Device Free Activity Recognition using Ultra-Wideband Radio Communication. [Internet] [Thesis]. University of Houston; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10657/3303.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sharma S1. Device Free Activity Recognition using Ultra-Wideband Radio Communication. [Thesis]. University of Houston; 2018. Available from: http://hdl.handle.net/10657/3303

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Nelson Mandela Metropolitan University

17. [No author]. A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills.

Degree: Faculty of Science, 2015, Nelson Mandela Metropolitan University

URL: http://hdl.handle.net/10948/6204

► Fundamental movement skills (FMSs) are considered to be one of the essential phases of motor skill development. The proper development of FMSs allows children to… (more)

▼ Fundamental movement skills (FMSs) are considered to be one of the essential phases of motor skill development. The proper development of FMSs allows children to participate in more advanced forms of movements and sports. To be able to perform an FMS correctly, children need to learn the right way of performing it. By making use of technology, a system can be developed that can help facilitate the learning of FMSs. The objective of the research was to propose an effective natural user interface (NUI) architecture for detecting FMSs using the Kinect. In order to achieve the stated objective, an investigation into FMSs and the challenges faced when teaching them was presented. An investigation into NUIs was also presented including the merits of the Kinect as the most appropriate device to be used to facilitate the detection of an FMS. An NUI architecture was proposed that uses the Kinect to facilitate the detection of an FMS. A framework was implemented from the design of the architecture. The successful implementation of the framework provides evidence that the design of the proposed architecture is feasible. An instance of the framework incorporating the jump FMS was used as a case study in the development of a prototype that detects the correct and incorrect performance of a jump. The evaluation of the prototype proved the following: - The developed prototype was effective in detecting the correct and incorrect performance of the jump FMS; and - The implemented framework was robust for the incorporation of an FMS. The successful implementation of the prototype shows that an effective NUI architecture using the Kinect can be used to facilitate the detection of FMSs. The proposed architecture provides a structured way of developing a system using the Kinect to facilitate the detection of FMSs. This allows developers to add future FMSs to the system. This dissertation therefore makes the following contributions: - An experimental design to evaluate the effectiveness of a prototype that detects FMSs - A robust framework that incorporates FMSs; and - An effective NUI architecture to facilitate the detection of fundamental movement skills using the Kinect.

Subjects/Keywords: Human activity recognition; Human-computer interaction

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APA (6^th Edition):

author], [. (2015). A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills. (Thesis). Nelson Mandela Metropolitan University. Retrieved from http://hdl.handle.net/10948/6204

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

author], [No. “A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills.” 2015. Thesis, Nelson Mandela Metropolitan University. Accessed December 15, 2019. http://hdl.handle.net/10948/6204.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

author], [No. “A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills.” 2015. Web. 15 Dec 2019.

Vancouver:

author] [. A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills. [Internet] [Thesis]. Nelson Mandela Metropolitan University; 2015. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10948/6204.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills. [Thesis]. Nelson Mandela Metropolitan University; 2015. Available from: http://hdl.handle.net/10948/6204

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. Zhu, Shangyue. Human activity localization and recognation based on radar sensors for smart homes.

Degree: Thesis (M.S.), 2017, Ball State University

URL: http://cardinalscholar.bsu.edu/handle/123456789/201073

► The smart home is going through a rapid development in which predicting behaviors provides convenient service in human daily life. Tracking a user and recognizing… (more)

Subjects/Keywords: Acoustic localization.; Human activity recognition.; Home automation.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhu, S. (2017). Human activity localization and recognation based on radar sensors for smart homes. (Masters Thesis). Ball State University. Retrieved from http://cardinalscholar.bsu.edu/handle/123456789/201073

Chicago Manual of Style (16^th Edition):

Zhu, Shangyue. “Human activity localization and recognation based on radar sensors for smart homes.” 2017. Masters Thesis, Ball State University. Accessed December 15, 2019. http://cardinalscholar.bsu.edu/handle/123456789/201073.

MLA Handbook (7^th Edition):

Zhu, Shangyue. “Human activity localization and recognation based on radar sensors for smart homes.” 2017. Web. 15 Dec 2019.

Vancouver:

Zhu S. Human activity localization and recognation based on radar sensors for smart homes. [Internet] [Masters thesis]. Ball State University; 2017. [cited 2019 Dec 15]. Available from: http://cardinalscholar.bsu.edu/handle/123456789/201073.

Council of Science Editors:

Zhu S. Human activity localization and recognation based on radar sensors for smart homes. [Masters Thesis]. Ball State University; 2017. Available from: http://cardinalscholar.bsu.edu/handle/123456789/201073

University of New South Wales

19. Wei, Bo. Embedded Sensing for Acoustic Classification, Activity Recognition and Localisation.

Degree: Computer Science & Engineering, 2015, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/54910 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36161/SOURCE02?view=true

► Embedded sensing aims to use low-cost computing, sensing and communication components to realise various sensing tasks. Embedded sensing has been successfully used in different applications.… (more)

▼ Embedded sensing aims to use low-cost computing, sensing and communication components to realise various sensing tasks. Embedded sensing has been successfully used in different applications. Large amounts of sensing data contain abundant information, but create huge overheads for the resource-limited embedded nodes. Furthermore, interference from other sources also brings noise, which decreases the sensing performance. In this thesis, we apply Sparse Approximation-based Classification method (SAC) and electronically switched directional (ESD) antennas to address the challenges of the limited amount of resources in embedded systems and interference. Three different problems are addressed. The first problem is to reduce the overhead of real-time classification on Acoustic Sensor Networks (ASNs). The main challenges of in-network classification in ASNs include effective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based featureless, low computational cost, and noise resilient framework for in-network classification in ASNs, which makes the computation feasible to be performed on resource constrained ASN platforms. The second problem is to make radio-based device-free activity recognition robust to radio frequency interference (RFI). Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. We investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We propose a number of SAC-based fusion methods to mitigate the impact of RFI and improve the location-oriented activity recognition performance. The third problem is to reduce the impact of multipath propagation for radio tomographic imaging (RTI) . RTI enables device-free localisation of people and objects in many challenging environments and situations. However, the localisation accuracy of RTI suffers from complicated multipath propagation behaviours in radio links. We propose to use inexpensive and energy efficient ESD antennas to improve the localisation accuracy of RTI. We implement a directional RTI system to understand how directional antennas can be used to improve RTI localisation accuracy. Advisors/Committee Members: Chou, Chun Tung, Computer Science & Engineering, Faculty of Engineering, UNSW, Hu, Wen, Computer Science & Engineering, Faculty of Engineering, UNSW.

Subjects/Keywords: Activity recognition; Embedded sensing; Acoustic classification; Localisation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wei, B. (2015). Embedded Sensing for Acoustic Classification, Activity Recognition and Localisation. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/54910 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36161/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Wei, Bo. “Embedded Sensing for Acoustic Classification, Activity Recognition and Localisation.” 2015. Doctoral Dissertation, University of New South Wales. Accessed December 15, 2019. http://handle.unsw.edu.au/1959.4/54910 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36161/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Wei, Bo. “Embedded Sensing for Acoustic Classification, Activity Recognition and Localisation.” 2015. Web. 15 Dec 2019.

Vancouver:

Wei B. Embedded Sensing for Acoustic Classification, Activity Recognition and Localisation. [Internet] [Doctoral dissertation]. University of New South Wales; 2015. [cited 2019 Dec 15]. Available from: http://handle.unsw.edu.au/1959.4/54910 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36161/SOURCE02?view=true.

Council of Science Editors:

Wei B. Embedded Sensing for Acoustic Classification, Activity Recognition and Localisation. [Doctoral Dissertation]. University of New South Wales; 2015. Available from: http://handle.unsw.edu.au/1959.4/54910 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36161/SOURCE02?view=true

University of Georgia

20. Aitha, Naveen Kumar. A hybrid multi-layer wavelet-based video encoding scheme for computer vision applications on mobile resource constrained devices.

Degree: MS, Computer Science, 2010, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/aitha_naveen-kumar_201012_ms

► The use of multimedia-enabled mobile devices such as pocket PC's, smart cell phones and PDA's is increasing by the day and at a rapid pace.… (more)

Subjects/Keywords: layered media; Video streaming; Activity Recognition; Face Recognition

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APA (6^th Edition):

Aitha, N. K. (2010). A hybrid multi-layer wavelet-based video encoding scheme for computer vision applications on mobile resource constrained devices. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/aitha_naveen-kumar_201012_ms

Chicago Manual of Style (16^th Edition):

Aitha, Naveen Kumar. “A hybrid multi-layer wavelet-based video encoding scheme for computer vision applications on mobile resource constrained devices.” 2010. Masters Thesis, University of Georgia. Accessed December 15, 2019. http://purl.galileo.usg.edu/uga_etd/aitha_naveen-kumar_201012_ms.

MLA Handbook (7^th Edition):

Aitha, Naveen Kumar. “A hybrid multi-layer wavelet-based video encoding scheme for computer vision applications on mobile resource constrained devices.” 2010. Web. 15 Dec 2019.

Vancouver:

Aitha NK. A hybrid multi-layer wavelet-based video encoding scheme for computer vision applications on mobile resource constrained devices. [Internet] [Masters thesis]. University of Georgia; 2010. [cited 2019 Dec 15]. Available from: http://purl.galileo.usg.edu/uga_etd/aitha_naveen-kumar_201012_ms.

Council of Science Editors:

Aitha NK. A hybrid multi-layer wavelet-based video encoding scheme for computer vision applications on mobile resource constrained devices. [Masters Thesis]. University of Georgia; 2010. Available from: http://purl.galileo.usg.edu/uga_etd/aitha_naveen-kumar_201012_ms

University of Adelaide

21. Ruan, Wenjie. Device-free human localization and activity recognition for supporting the independent living of the elderly.

Degree: 2017, University of Adelaide

URL: http://hdl.handle.net/2440/112860

► Given the continuous growth of the aging population, the cost of health care, and the preference that the elderly want to live independently and safely… (more)

▼ Given the continuous growth of the aging population, the cost of health care, and the preference that the elderly want to live independently and safely at their own homes, the demand on developing an innovative living-assistive system to facilitate the independent living for the elderly is becoming increasingly urgent. This novel system is envisioned to be device-free, intelligent, and maintenance-free as well as deployable in a residential environment. The key to realizing such envisioned system is to study low cost sensor technologies that are practical for device-free human indoor localization and activity recognition, particularly under a clustered residential home. By exploring the latest, low-cost and unobtrusive RFID sensor technology, this thesis intends to design a new device-free system for better supporting the independent living of the elderly. Arising from this live-assistive system, this thesis specifically targets the following six research problems. Firstly, to deal with severe missing readings of passive RFID tags, this thesis proposes a novel tensor-based low-rank sensor reading recovery method, in which we formulate RFID sensor data as a high-dimensional tensor that can naturally preserve sensors’ spatial and temporal information. Secondly, by purely using passive RFID hardware, we build a novel data-driven device-free localization and tracking system. We formulate human localization problem as finding a location with the maximum posterior probability given the observed RSSIs (Received Signal Strength Indicator) from passive RFID tags. For tracking a moving target, we mathematically model the task as searching a location sequence with the most likelihood under a Hidden Markov Model (HMM) framework. Thirdly, to tackle the challenge that the tracking accuracy decreases in a cluttered residential environment, we propose to leverage the Human-Object Interaction (HOI) events to enhance the performance of the proposed RFID-based system. This idea is motivated by an intuition that HOI events, detected by pervasive sensors, can potentially reveal people’s interleaved locations during daily living activities such as watching TV or opening the fridge door. Furthermore, to recognize the resident’s daily activities, we propose a device-free human activity recognition (HAR) system by deploying the passive RFID tags as an array attached on the wall. This HAR system operates by learning how RSSIs are distributed when a resident performs different activities. Moreover, considering that falls are among the leading causes of hospitalization for the elderly, we develop a fine-grained fall detection system that is capable of not only recognizing regular actions and fall events simultaneously, but also sensing the fine-grained fall orientations. Lastly, to remotely control the smart electronic appliances equipped in an intelligent environment, we design a device-free multi-modal hand gesture recognition (HGR) system that can accurately sense the hand’s in-air speed, waving direction, moving range and duration around a… Advisors/Committee Members: Sheng, Michael (advisor), Falkner, Nickolas John Gowland (advisor), Yao, Lina (advisor), Li, Xue (advisor), School of Computer Science (school).

Subjects/Keywords: indoor localization; human activity recognition; RFID; hand gesture recognition; tensor; decomposition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ruan, W. (2017). Device-free human localization and activity recognition for supporting the independent living of the elderly. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/112860

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ruan, Wenjie. “Device-free human localization and activity recognition for supporting the independent living of the elderly.” 2017. Thesis, University of Adelaide. Accessed December 15, 2019. http://hdl.handle.net/2440/112860.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ruan, Wenjie. “Device-free human localization and activity recognition for supporting the independent living of the elderly.” 2017. Web. 15 Dec 2019.

Vancouver:

Ruan W. Device-free human localization and activity recognition for supporting the independent living of the elderly. [Internet] [Thesis]. University of Adelaide; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2440/112860.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ruan W. Device-free human localization and activity recognition for supporting the independent living of the elderly. [Thesis]. University of Adelaide; 2017. Available from: http://hdl.handle.net/2440/112860

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Hong Kong University of Science and Technology

22. Sun, Lin ECE. Deeply learned representations for human action recognition.

Degree: 2018, Hong Kong University of Science and Technology

URL: https://doi.org/10.14711/thesis-991012637468003412 ; http://repository.ust.hk/ir/bitstream/1783.1-96003/1/th_redirect.html

► Unlike in image recognition, human actions in video sequences are three-dimensional (3D) spatio-temporal signals characterizing both the visual appearance and motion dynamics of the involved… (more)

▼ Unlike in image recognition, human actions in video sequences are three-dimensional (3D) spatio-temporal signals characterizing both the visual appearance and motion dynamics of the involved humans and objects. Previous research has mainly focused on using hand-designed local features, such as SIFT, HOG and SURF, to solve the video-based recognition problem. However, these approaches have complex implementation and are difficult to extend to the real-world data. Inspired by the success of deeply learned features for image classification, recent attempts have been made to learn deep features for video analysis. However, unlike image analysis, few deep learning models have been proposed to solve the problems in video analysis, and only limited success for videos has been reported. In particular, most such models either deal with simple datasets or rely on low-level local spatial-temporal features for the final precision. Transferring the success of two-dimensional (2D) Convolutional Neural Networks (CNNs) to videos by implementing 3D CNNs is a direct approach for action recognition. However, partially due to the high complexity of training 3D convolution kernels and the need for large quantities of training videos, only limited success has been reported. Therefore, we investigate a new deep architecture which can handle 3D signals more effectively. We propose a factorized spatio-temporal convolutional network (F_STCN) structure which factorizes the original 3D convolution kernel learning as a sequential process of learning 2D spatial kernels in the lower layers (called spatial convolutional layers), followed by learning 1D temporal kernels in the upper layers (called temporal convolutional layers). In order to enhance the spatio-temporal representations for videos without losing the advantage of speed, we propose to add another modality, the difference between neighboring RGB frames, into the spatio-temporal modeling. CNN-based methods are effective in learning spatial appearances, but are limited in modeling long-term motion dynamics. On the other hand, Recurrent Neural Networks (RNNs), are able to learn temporal motion dynamics though iteratively feeding the previous hidden features. In this thesis, we present RNNs as an alternative approach to CNNs. We establish that a feedback based approach, such as RNNs has several fundamental advantages over feedforward approach besides the comparable performance. We further apply RNNs, particularly the long short-term memory (LSTM) to human action recognition problems. In our experiments, we find that compared with CNNs, RNNs can better model the temporal relations in videos. However, naively applying RNNs to video sequences in a convolutional manner implicitly assumes that motions in videos are stationary across different spatial locations. This assumption is valid for short-term motions but invalid when the duration of the motion is long. To address this invalid assumption, we propose the Lattice-LSTM (L²STM), which extends the LSTM by learning…

Subjects/Keywords: Human activity recognition; Data processing; Pattern recognition systems; Computer vision

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sun, L. E. (2018). Deeply learned representations for human action recognition. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-991012637468003412 ; http://repository.ust.hk/ir/bitstream/1783.1-96003/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sun, Lin ECE. “Deeply learned representations for human action recognition.” 2018. Thesis, Hong Kong University of Science and Technology. Accessed December 15, 2019. https://doi.org/10.14711/thesis-991012637468003412 ; http://repository.ust.hk/ir/bitstream/1783.1-96003/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sun, Lin ECE. “Deeply learned representations for human action recognition.” 2018. Web. 15 Dec 2019.

Vancouver:

Sun LE. Deeply learned representations for human action recognition. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2018. [cited 2019 Dec 15]. Available from: https://doi.org/10.14711/thesis-991012637468003412 ; http://repository.ust.hk/ir/bitstream/1783.1-96003/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sun LE. Deeply learned representations for human action recognition. [Thesis]. Hong Kong University of Science and Technology; 2018. Available from: https://doi.org/10.14711/thesis-991012637468003412 ; http://repository.ust.hk/ir/bitstream/1783.1-96003/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

23. Yu, Wenbo. Good-walk recognition using Android smartphone accelerometer with application on senior patients.

Degree: MS, Computer Science, 2016, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/90611

► Good walk from one's everyday activities can be used towards chronic disease diagnosis. Smartphones have become increasingly popular among people across ages. Properties including light… (more)

Subjects/Keywords: bioinformatics; walk recognition; activity recognition; smartphone; accelerometer; senior patient

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APA (6^th Edition):

Yu, W. (2016). Good-walk recognition using Android smartphone accelerometer with application on senior patients. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90611

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yu, Wenbo. “Good-walk recognition using Android smartphone accelerometer with application on senior patients.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed December 15, 2019. http://hdl.handle.net/2142/90611.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yu, Wenbo. “Good-walk recognition using Android smartphone accelerometer with application on senior patients.” 2016. Web. 15 Dec 2019.

Vancouver:

Yu W. Good-walk recognition using Android smartphone accelerometer with application on senior patients. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2142/90611.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu W. Good-walk recognition using Android smartphone accelerometer with application on senior patients. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90611

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

24. Banerjee, Prithviraj. Incorporating aggregate feature statistics in structured dynamical models for human activity recognition.

Degree: PhD, Computer Science, 2014, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3438

► Human action recognition in videos is a central problem of computer vision, with numerous applications in the fields of video surveillance, data mining and human… (more)

▼ Human action recognition in videos is a central problem of computer vision, with numerous applications in the fields of video surveillance, data mining and human computer interaction. There has been considerable research in classifying pre-segmented videos into a single activity class, however there has been comparatively less progress on activity detection in un-segmented and un-aligned videos containing medium to long term complex events. ❧ Our objective is to develop efficient algorithms to recognize human activities in monocular videos captured from static cameras in both indoor and outdoor scenarios. Our focus is on detection and classification of complex human events in un-segmented continuous videos, where the top level event is composed of primitive action components, such as human key-poses or primitive actions. We assume a weakly supervised setting, where only the top level event labels are provided for each video during training, and the primitive actions are not labeled. We require our algorithm to be robust to missing frames, temporary occlusion of body parts, background clutter, and to variations in activity styles and durations. Furthermore, our models gracefully scale to complex events containing human-human and human-object interactions, while not assuming access to perfect pedestrian or object detection results. ❧ We have proposed and adopted the design philosophy of combining global statistics of local spatio-temporal features, with the high level structure and constraints provided by dynamic probabilistic graphical models. We present four different algorithms for activity recognition, spanning the feature-classifier hierarchy in terms of their semantic and structure modeling capability. Firstly, we present a novel Latent CRF classifier for modeling the local neighborhood structure of spatio-temporal interest point features in terms of code-word co-occurrence statistics, which captures the local temporal dynamics present in the action. In our second work, we present a multiple kernel learning framework to combine human pose estimates generated from a collection of kinematic tree priors, spanning the range of expected pose dynamics in human actions. In our third work, we present a latent CRF model for automatically identifying and inferring the temporal location of key-poses of an activity, and show results on detecting multiple instances of actions in continuous un-segmented videos. Lastly, we propose a novel dynamic multi-state feature pooling algorithm which identifies the discriminative segments of a video, and is robust to arbitrary gaps between state transitions, and also to significant variations in state durations. We evaluate our models on short, medium and long term activity datasets and show state of the art performance on both classification, detection and video streaming tasks. Advisors/Committee Members: Nevatia, Ramakant (Committee Chair), Medioni, Gerard G. (Committee Member), Medioni, Gérard G. (Committee Member), Liu, Yan (Committee Member), Shen, Wei-Min (Committee Member), Kuo, C.-C. Jay (Committee Member).

Subjects/Keywords: computer vision; machine learning; human activity recognition; activity detection; graphical models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Banerjee, P. (2014). Incorporating aggregate feature statistics in structured dynamical models for human activity recognition. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3438

Chicago Manual of Style (16^th Edition):

Banerjee, Prithviraj. “Incorporating aggregate feature statistics in structured dynamical models for human activity recognition.” 2014. Doctoral Dissertation, University of Southern California. Accessed December 15, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3438.

MLA Handbook (7^th Edition):

Banerjee, Prithviraj. “Incorporating aggregate feature statistics in structured dynamical models for human activity recognition.” 2014. Web. 15 Dec 2019.

Vancouver:

Banerjee P. Incorporating aggregate feature statistics in structured dynamical models for human activity recognition. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Dec 15]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3438.

Council of Science Editors:

Banerjee P. Incorporating aggregate feature statistics in structured dynamical models for human activity recognition. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3438

Washington State University

25. [No author]. Scaling Activity Discovery and Recognition to Large, Complex Datasets .

Degree: 2011, Washington State University

URL: http://hdl.handle.net/2376/2861

► In the past decade, activity discovery and recognition has been studied by many researchers. However there are still many challenges to be addressed before deploying… (more)

Subjects/Keywords: Computer Science; Activity Discovery; Activity Recognition; Data Mining; Machine Learning

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APA (6^th Edition):

author], [. (2011). Scaling Activity Discovery and Recognition to Large, Complex Datasets . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/2861

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

author], [No. “Scaling Activity Discovery and Recognition to Large, Complex Datasets .” 2011. Thesis, Washington State University. Accessed December 15, 2019. http://hdl.handle.net/2376/2861.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

author], [No. “Scaling Activity Discovery and Recognition to Large, Complex Datasets .” 2011. Web. 15 Dec 2019.

Vancouver:

author] [. Scaling Activity Discovery and Recognition to Large, Complex Datasets . [Internet] [Thesis]. Washington State University; 2011. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2376/2861.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Scaling Activity Discovery and Recognition to Large, Complex Datasets . [Thesis]. Washington State University; 2011. Available from: http://hdl.handle.net/2376/2861

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Colorado School of Mines

26. Reily, Brian J. Human activity recognition and gymnastics analysis through depth imagery.

Degree: MS(M.S.), Electrical Engineering and Computer Science, 2016, Colorado School of Mines

URL: http://hdl.handle.net/11124/170153

► Depth imagery is transforming many areas of computer vision, such as object recognition, human detection, human activity recognition, and sports analysis. The goal of my… (more)

Subjects/Keywords: Activity prediction; Activity recognition; Depth imagery; Gymnastics; Image segmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Reily, B. J. (2016). Human activity recognition and gymnastics analysis through depth imagery. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/170153

Chicago Manual of Style (16^th Edition):

Reily, Brian J. “Human activity recognition and gymnastics analysis through depth imagery.” 2016. Masters Thesis, Colorado School of Mines. Accessed December 15, 2019. http://hdl.handle.net/11124/170153.

MLA Handbook (7^th Edition):

Reily, Brian J. “Human activity recognition and gymnastics analysis through depth imagery.” 2016. Web. 15 Dec 2019.

Vancouver:

Reily BJ. Human activity recognition and gymnastics analysis through depth imagery. [Internet] [Masters thesis]. Colorado School of Mines; 2016. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/11124/170153.

Council of Science Editors:

Reily BJ. Human activity recognition and gymnastics analysis through depth imagery. [Masters Thesis]. Colorado School of Mines; 2016. Available from: http://hdl.handle.net/11124/170153

University of Plymouth

27. Al-Obaidi, Hind K. Transparent authentication utilising gait recognition.

Degree: PhD, 2019, University of Plymouth

URL: http://hdl.handle.net/10026.1/14898

► Securing smartphones has increasingly become inevitable due to their massive popularity and significant storage and access to sensitive information. The gatekeeper of securing the device… (more)

▼ Securing smartphones has increasingly become inevitable due to their massive popularity and significant storage and access to sensitive information. The gatekeeper of securing the device is authenticating the user. Amongst the many solutions proposed, gait recognition has been suggested to provide a reliable yet non-intrusive authentication approach - enabling both security and usability. While several studies exploring mobile-based gait recognition have taken place, studies have been mainly preliminary, with various methodological restrictions that have limited the number of participants, samples, and type of features; in addition, prior studies have depended on limited datasets, actual controlled experimental environments, and many activities. They suffered from the absence of real-world datasets, which lead to verify individuals incorrectly. This thesis has sought to overcome these weaknesses and provide, a comprehensive evaluation, including an analysis of smartphone-based motion sensors (accelerometer and gyroscope), understanding the variability of feature vectors during differing activities across a multi-day collection involving 60 participants. This framed into two experiments involving five types of activities: standard, fast, with a bag, downstairs, and upstairs walking. The first experiment explores the classification performance in order to understand whether a single classifier or multi-algorithmic approach would provide a better level of performance. The second experiment investigated the feature vector (comprising of a possible 304 unique features) to understand how its composition affects performance and for a comparison a more particular set of the minimal features are involved. The controlled dataset achieved performance exceeded the prior work using same and cross day methodologies (e.g., for the regular walk activity, the best results EER of 0.70% and EER of 6.30% for the same and cross day scenarios respectively). Moreover, multi-algorithmic approach achieved significant improvement over the single classifier approach and thus a more practical approach to managing the problem of feature vector variability. An Activity recognition model was applied to the real-life gait dataset containing a more significant number of gait samples employed from 44 users (7-10 days for each user). A human physical motion activity identification modelling was built to classify a given individual's activity signal into a predefined class belongs to. As such, the thesis implemented a novel real-world gait recognition system that recognises the subject utilising smartphone-based real-world dataset. It also investigates whether these authentication technologies can recognise the genuine user and rejecting an imposter. Real dataset experiment results are offered a promising level of security particularly when the majority voting techniques were applied. As well as, the proposed multi-algorithmic approach seems to be more reliable and tends to perform relatively well in practice on real live user data, an improved model…

Subjects/Keywords: gait activity; smartphone sensors; gyroscope; accelerometer; human activity recognition; mobile authentication

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Al-Obaidi, H. K. (2019). Transparent authentication utilising gait recognition. (Doctoral Dissertation). University of Plymouth. Retrieved from http://hdl.handle.net/10026.1/14898

Chicago Manual of Style (16^th Edition):

Al-Obaidi, Hind K. “Transparent authentication utilising gait recognition.” 2019. Doctoral Dissertation, University of Plymouth. Accessed December 15, 2019. http://hdl.handle.net/10026.1/14898.

MLA Handbook (7^th Edition):

Al-Obaidi, Hind K. “Transparent authentication utilising gait recognition.” 2019. Web. 15 Dec 2019.

Vancouver:

Al-Obaidi HK. Transparent authentication utilising gait recognition. [Internet] [Doctoral dissertation]. University of Plymouth; 2019. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10026.1/14898.

Council of Science Editors:

Al-Obaidi HK. Transparent authentication utilising gait recognition. [Doctoral Dissertation]. University of Plymouth; 2019. Available from: http://hdl.handle.net/10026.1/14898

Loughborough University

28. Aguilar Grajeda, Ricardo. Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring.

Degree: PhD, 2018, Loughborough University

URL: https://dspace.lboro.ac.uk/2134/37079 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771260

► Health authorities in numerous countries and even the World Health Organization (WHO) are concerned with low levels of physical activity and increasing sedentary behaviour amongst… (more)

▼ Health authorities in numerous countries and even the World Health Organization (WHO) are concerned with low levels of physical activity and increasing sedentary behaviour amongst the general population. In fact, emerging evidences identify sedentary behaviour as a ubiquitous characteristic of contemporary lifestyles. This has major implications for the general health of people worldwide particularly for the prevalence of non-communicable conditions (NCDs) such as cardiovascular disease, diabetes and cancer and their risk factors such as raised blood pressure, raised blood sugar and overweight. Moreover, sedentary time appears to be uniquely associated with health risks independent of physical activity intensity levels. However, habitual sedentary behaviour may prove complex to be accurately measured as it occurs across different domains, including work, transport, domestic duties and even lei¬sure. Since sedentary behaviour is mostly reflect as too much sitting, one of the main concerns is being able to distinguish among different activities, such as sitting and standing. Widely used devices such as accelerometer-based activity monitors have a limited ability to detect sedentary activities accurately. Thus, there is a need of a viable large-scale method to efficiently monitor sedentary behaviour. This thesis proposes and demonstrates how a plantar pressure based wearable device and machine learning classification techniques have significant capability to monitor daily life sedentary behaviour. Firstly, an in-depth review of research and market ready plantar pressure and force technologies is performed to assess their measurement capabilities and limitations to measure sedentary behaviour. Afterwards, a novel methodology for measuring daily life sedentary behaviour using plantar pressure data and a machine learning predictive model is developed. The proposed model and its algorithm are constructed using a dataset of 20 participants collected at both laboratory-based and free-living conditions. Sitting and standing variations are included in the analysis as well as the addition of a potential novel activities, such as leaning. Video footage is continuously collected using of a wearable camera as an equivalent of direct observation to allow the labelling of the training data for the machine learning model. The optimal parameters of the model such as feature set, epoch length, type of classifier is determined by experimenting with multiple iterations. Different number and location of plantar pressure sensors are explored to determine the optimal trade-off between low computational cost and accurate performance. The model s performance is calculated using both subject dependent and subject independent validation by performing 10-fold stratified cross-validation and leave-one-user-out validation respectively. Furthermore, the proposed model activity performance for daily life monitoring is validated against the current criterion (i.e. direct observation) and against the de facto standard, the activPAL. The results show…

Subjects/Keywords: Posture recognition; Activity recognition; Sedentary behaviour; Physical activity; Wearable sensors; Pattern recognition; Machine learning; Plantar pressure; Sitting time; Free-living

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Aguilar Grajeda, R. (2018). Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring. (Doctoral Dissertation). Loughborough University. Retrieved from https://dspace.lboro.ac.uk/2134/37079 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771260

Chicago Manual of Style (16^th Edition):

Aguilar Grajeda, Ricardo. “Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring.” 2018. Doctoral Dissertation, Loughborough University. Accessed December 15, 2019. https://dspace.lboro.ac.uk/2134/37079 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771260.

MLA Handbook (7^th Edition):

Aguilar Grajeda, Ricardo. “Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring.” 2018. Web. 15 Dec 2019.

Vancouver:

Aguilar Grajeda R. Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring. [Internet] [Doctoral dissertation]. Loughborough University; 2018. [cited 2019 Dec 15]. Available from: https://dspace.lboro.ac.uk/2134/37079 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771260.

Council of Science Editors:

Aguilar Grajeda R. Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring. [Doctoral Dissertation]. Loughborough University; 2018. Available from: https://dspace.lboro.ac.uk/2134/37079 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771260

Loughborough University

29. Aguilar Grajeda, Ricardo. Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring.

Degree: PhD, 2018, Loughborough University

URL: http://hdl.handle.net/2134/37079

► Health authorities in numerous countries and even the World Health Organization (WHO) are concerned with low levels of physical activity and increasing sedentary behaviour amongst… (more)

▼ Health authorities in numerous countries and even the World Health Organization (WHO) are concerned with low levels of physical activity and increasing sedentary behaviour amongst the general population. In fact, emerging evidences identify sedentary behaviour as a ubiquitous characteristic of contemporary lifestyles. This has major implications for the general health of people worldwide particularly for the prevalence of non-communicable conditions (NCDs) such as cardiovascular disease, diabetes and cancer and their risk factors such as raised blood pressure, raised blood sugar and overweight. Moreover, sedentary time appears to be uniquely associated with health risks independent of physical activity intensity levels. However, habitual sedentary behaviour may prove complex to be accurately measured as it occurs across different domains, including work, transport, domestic duties and even lei¬sure. Since sedentary behaviour is mostly reflect as too much sitting, one of the main concerns is being able to distinguish among different activities, such as sitting and standing. Widely used devices such as accelerometer-based activity monitors have a limited ability to detect sedentary activities accurately. Thus, there is a need of a viable large-scale method to efficiently monitor sedentary behaviour. This thesis proposes and demonstrates how a plantar pressure based wearable device and machine learning classification techniques have significant capability to monitor daily life sedentary behaviour. Firstly, an in-depth review of research and market ready plantar pressure and force technologies is performed to assess their measurement capabilities and limitations to measure sedentary behaviour. Afterwards, a novel methodology for measuring daily life sedentary behaviour using plantar pressure data and a machine learning predictive model is developed. The proposed model and its algorithm are constructed using a dataset of 20 participants collected at both laboratory-based and free-living conditions. Sitting and standing variations are included in the analysis as well as the addition of a potential novel activities, such as leaning. Video footage is continuously collected using of a wearable camera as an equivalent of direct observation to allow the labelling of the training data for the machine learning model. The optimal parameters of the model such as feature set, epoch length, type of classifier is determined by experimenting with multiple iterations. Different number and location of plantar pressure sensors are explored to determine the optimal trade-off between low computational cost and accurate performance. The model s performance is calculated using both subject dependent and subject independent validation by performing 10-fold stratified cross-validation and leave-one-user-out validation respectively. Furthermore, the proposed model activity performance for daily life monitoring is validated against the current criterion (i.e. direct observation) and against the de facto standard, the activPAL. The results show…

Subjects/Keywords: Posture recognition; Activity recognition; Sedentary behaviour; Physical activity; Wearable sensors; Pattern recognition; Machine learning; Plantar pressure; Sitting time; Free-living

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Aguilar Grajeda, R. (2018). Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring. (Doctoral Dissertation). Loughborough University. Retrieved from http://hdl.handle.net/2134/37079

Chicago Manual of Style (16^th Edition):

Aguilar Grajeda, Ricardo. “Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring.” 2018. Doctoral Dissertation, Loughborough University. Accessed December 15, 2019. http://hdl.handle.net/2134/37079.

MLA Handbook (7^th Edition):

Aguilar Grajeda, Ricardo. “Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring.” 2018. Web. 15 Dec 2019.

Vancouver:

Aguilar Grajeda R. Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring. [Internet] [Doctoral dissertation]. Loughborough University; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2134/37079.

Council of Science Editors:

Aguilar Grajeda R. Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring. [Doctoral Dissertation]. Loughborough University; 2018. Available from: http://hdl.handle.net/2134/37079

30. Gani, Md Osman. A Novel Approach to Complex Human Activity Recognition.

Degree: 2017, Marquette University

URL: https://epublications.marquette.edu/dissertations_mu/701

► Human activity recognition is a technology that offers automatic recognition of what a person is doing with respect to body motion and function. The main… (more)

Subjects/Keywords: Complex Human Activity; Human Activity Recognition; Localization; Simple Human Activity; Computer Sciences; Hardware Systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Gani, M. O. (2017). A Novel Approach to Complex Human Activity Recognition. (Thesis). Marquette University. Retrieved from https://epublications.marquette.edu/dissertations_mu/701

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Gani, Md Osman. “A Novel Approach to Complex Human Activity Recognition.” 2017. Thesis, Marquette University. Accessed December 15, 2019. https://epublications.marquette.edu/dissertations_mu/701.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Gani, Md Osman. “A Novel Approach to Complex Human Activity Recognition.” 2017. Web. 15 Dec 2019.

Vancouver:

Gani MO. A Novel Approach to Complex Human Activity Recognition. [Internet] [Thesis]. Marquette University; 2017. [cited 2019 Dec 15]. Available from: https://epublications.marquette.edu/dissertations_mu/701.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gani MO. A Novel Approach to Complex Human Activity Recognition. [Thesis]. Marquette University; 2017. Available from: https://epublications.marquette.edu/dissertations_mu/701

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations