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You searched for subject:(Human Activity Recognition). Showing records 1 – 30 of 125 total matches.

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Hong Kong University of Science and Technology

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

Degree: 2012, Hong Kong University of Science and Technology

 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)

Subjects/Keywords: Human activity recognition ; Machine learning

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

APA (6th Edition):

Hu, H. (2012). Learning-based human activity recognition. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-7811 ; 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 (16th Edition):

Hu, Hao. “Learning-based human activity recognition.” 2012. Thesis, Hong Kong University of Science and Technology. Accessed November 28, 2020. http://repository.ust.hk/ir/Record/1783.1-7811 ; 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 (7th Edition):

Hu, Hao. “Learning-based human activity recognition.” 2012. Web. 28 Nov 2020.

Vancouver:

Hu H. Learning-based human activity recognition. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2012. [cited 2020 Nov 28]. Available from: http://repository.ust.hk/ir/Record/1783.1-7811 ; 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: http://repository.ust.hk/ir/Record/1783.1-7811 ; 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


Nelson Mandela Metropolitan University

2. [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

 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)

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

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

APA (6th 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 (16th 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 November 28, 2020. 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 (7th Edition):

author], [No. “A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills.” 2015. Web. 28 Nov 2020.

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 2020 Nov 28]. 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

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

Degree: 2017, Marquette University

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 (6th 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 (16th Edition):

Gani, Md Osman. “A Novel Approach to Complex Human Activity Recognition.” 2017. Thesis, Marquette University. Accessed November 28, 2020. 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 (7th Edition):

Gani, Md Osman. “A Novel Approach to Complex Human Activity Recognition.” 2017. Web. 28 Nov 2020.

Vancouver:

Gani MO. A Novel Approach to Complex Human Activity Recognition. [Internet] [Thesis]. Marquette University; 2017. [cited 2020 Nov 28]. 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


Georgia Tech

4. Haresamudram, Harish. The role of representations in human activity recognition.

Degree: MS, Electrical and Computer Engineering, 2019, Georgia Tech

 We investigate the role of representations in sensor based human activity recognition (HAR). In particular, we develop convolutional and recurrent autoencoder architectures for feature learning… (more)

Subjects/Keywords: Unsupervised learning; Human activity recognition; Autoencoder models

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APA (6th Edition):

Haresamudram, H. (2019). The role of representations in human activity recognition. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62706

Chicago Manual of Style (16th Edition):

Haresamudram, Harish. “The role of representations in human activity recognition.” 2019. Masters Thesis, Georgia Tech. Accessed November 28, 2020. http://hdl.handle.net/1853/62706.

MLA Handbook (7th Edition):

Haresamudram, Harish. “The role of representations in human activity recognition.” 2019. Web. 28 Nov 2020.

Vancouver:

Haresamudram H. The role of representations in human activity recognition. [Internet] [Masters thesis]. Georgia Tech; 2019. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/1853/62706.

Council of Science Editors:

Haresamudram H. The role of representations in human activity recognition. [Masters Thesis]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62706

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

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

 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 (6th 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 (16th Edition):

Zhu, Shangyue. “Human activity localization and recognation based on radar sensors for smart homes.” 2017. Masters Thesis, Ball State University. Accessed November 28, 2020. http://cardinalscholar.bsu.edu/handle/123456789/201073.

MLA Handbook (7th Edition):

Zhu, Shangyue. “Human activity localization and recognation based on radar sensors for smart homes.” 2017. Web. 28 Nov 2020.

Vancouver:

Zhu S. Human activity localization and recognation based on radar sensors for smart homes. [Internet] [Masters thesis]. Ball State University; 2017. [cited 2020 Nov 28]. 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


Massey University

6. 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

 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 (6th 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 (16th 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 November 28, 2020. 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 (7th 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. 28 Nov 2020.

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 2020 Nov 28]. 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

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

Degree: Electrical Engineering, 2014, Oklahoma State University

 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)

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 (6th 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 (16th Edition):

Kallur, Dharmendra Chandrashekar. “Human Localization and Activity Recognition Using Distributed Motion Sensors.” 2014. Thesis, Oklahoma State University. Accessed November 28, 2020. 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 (7th Edition):

Kallur, Dharmendra Chandrashekar. “Human Localization and Activity Recognition Using Distributed Motion Sensors.” 2014. Web. 28 Nov 2020.

Vancouver:

Kallur DC. Human Localization and Activity Recognition Using Distributed Motion Sensors. [Internet] [Thesis]. Oklahoma State University; 2014. [cited 2020 Nov 28]. 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


California State University – Sacramento

8. Ghorpade, Madhuri. A novel semi-supervised learning framework for new human activity recognition.

Degree: MS, Computer Science, 2020, California State University – Sacramento

Human Activity Recognition (HAR) has been an attractive research topic for its applications in areas such as healthcare, smart environments, assisted living, home monitoring, personal… (more)

Subjects/Keywords: Human activity recognition; Semi supervised based learning; Embedding based human activity recognition

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APA (6th Edition):

Ghorpade, M. (2020). A novel semi-supervised learning framework for new human activity recognition. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/217496

Chicago Manual of Style (16th Edition):

Ghorpade, Madhuri. “A novel semi-supervised learning framework for new human activity recognition.” 2020. Masters Thesis, California State University – Sacramento. Accessed November 28, 2020. http://hdl.handle.net/10211.3/217496.

MLA Handbook (7th Edition):

Ghorpade, Madhuri. “A novel semi-supervised learning framework for new human activity recognition.” 2020. Web. 28 Nov 2020.

Vancouver:

Ghorpade M. A novel semi-supervised learning framework for new human activity recognition. [Internet] [Masters thesis]. California State University – Sacramento; 2020. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10211.3/217496.

Council of Science Editors:

Ghorpade M. A novel semi-supervised learning framework for new human activity recognition. [Masters Thesis]. California State University – Sacramento; 2020. Available from: http://hdl.handle.net/10211.3/217496


University of Adelaide

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

Degree: 2017, University of Adelaide

 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)

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

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APA (6th 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 (16th Edition):

Ruan, Wenjie. “Device-free human localization and activity recognition for supporting the independent living of the elderly.” 2017. Thesis, University of Adelaide. Accessed November 28, 2020. 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 (7th Edition):

Ruan, Wenjie. “Device-free human localization and activity recognition for supporting the independent living of the elderly.” 2017. Web. 28 Nov 2020.

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 2020 Nov 28]. 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

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

Degree: 2018, Hong Kong University of Science and Technology

 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)

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

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APA (6th Edition):

Sun, L. E. (2018). Deeply learned representations for human action recognition. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-96003 ; 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 (16th Edition):

Sun, Lin ECE. “Deeply learned representations for human action recognition.” 2018. Thesis, Hong Kong University of Science and Technology. Accessed November 28, 2020. http://repository.ust.hk/ir/Record/1783.1-96003 ; 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 (7th Edition):

Sun, Lin ECE. “Deeply learned representations for human action recognition.” 2018. Web. 28 Nov 2020.

Vancouver:

Sun LE. Deeply learned representations for human action recognition. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2018. [cited 2020 Nov 28]. Available from: http://repository.ust.hk/ir/Record/1783.1-96003 ; 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: http://repository.ust.hk/ir/Record/1783.1-96003 ; 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 North Texas

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

Degree: 2015, University of North Texas

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

APA (6th 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 (16th Edition):

Janmohammadi, Siamak. “Classifying Pairwise Object Interactions: A Trajectory Analytics Approach.” 2015. Thesis, University of North Texas. Accessed November 28, 2020. 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 (7th Edition):

Janmohammadi, Siamak. “Classifying Pairwise Object Interactions: A Trajectory Analytics Approach.” 2015. Web. 28 Nov 2020.

Vancouver:

Janmohammadi S. Classifying Pairwise Object Interactions: A Trajectory Analytics Approach. [Internet] [Thesis]. University of North Texas; 2015. [cited 2020 Nov 28]. 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

12. Santiteerakul, Wasana. Trajectory Analytics.

Degree: 2015, University of North Texas

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

APA (6th 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 (16th Edition):

Santiteerakul, Wasana. “Trajectory Analytics.” 2015. Thesis, University of North Texas. Accessed November 28, 2020. 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 (7th Edition):

Santiteerakul, Wasana. “Trajectory Analytics.” 2015. Web. 28 Nov 2020.

Vancouver:

Santiteerakul W. Trajectory Analytics. [Internet] [Thesis]. University of North Texas; 2015. [cited 2020 Nov 28]. 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


University of Cincinnati

13. Snyder, Kristian. Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting.

Degree: MS, Engineering and Applied Science: Computer Science, 2020, University of Cincinnati

 Classification of specialized human activity datasets utilizing methods not requiring manual feature extraction is an underserved area of research in the field of human activity(more)

Subjects/Keywords: Artificial Intelligence; activity recognition; back pain; accelerometer; convolutional neural network; deep learning; human activity recognition

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APA (6th Edition):

Snyder, K. (2020). Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255

Chicago Manual of Style (16th Edition):

Snyder, Kristian. “Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting.” 2020. Masters Thesis, University of Cincinnati. Accessed November 28, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255.

MLA Handbook (7th Edition):

Snyder, Kristian. “Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting.” 2020. Web. 28 Nov 2020.

Vancouver:

Snyder K. Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting. [Internet] [Masters thesis]. University of Cincinnati; 2020. [cited 2020 Nov 28]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255.

Council of Science Editors:

Snyder K. Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting. [Masters Thesis]. University of Cincinnati; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255

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

Degree: PhD, 2019, University of Plymouth

 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)

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

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APA (6th 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 (16th Edition):

Al-Obaidi, Hind K. “Transparent authentication utilising gait recognition.” 2019. Doctoral Dissertation, University of Plymouth. Accessed November 28, 2020. http://hdl.handle.net/10026.1/14898.

MLA Handbook (7th Edition):

Al-Obaidi, Hind K. “Transparent authentication utilising gait recognition.” 2019. Web. 28 Nov 2020.

Vancouver:

Al-Obaidi HK. Transparent authentication utilising gait recognition. [Internet] [Doctoral dissertation]. University of Plymouth; 2019. [cited 2020 Nov 28]. 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


University of Southern California

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

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

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)

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 (6th 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/3444

Chicago Manual of Style (16th Edition):

Banerjee, Prithviraj. “Incorporating aggregate feature statistics in structured dynamical models for human activity recognition.” 2014. Doctoral Dissertation, University of Southern California. Accessed November 28, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3444.

MLA Handbook (7th Edition):

Banerjee, Prithviraj. “Incorporating aggregate feature statistics in structured dynamical models for human activity recognition.” 2014. Web. 28 Nov 2020.

Vancouver:

Banerjee P. Incorporating aggregate feature statistics in structured dynamical models for human activity recognition. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2020 Nov 28]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3444.

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/3444


Columbia University

16. Barsoum, Emad. Human Motion Anticipation and Recognition from RGB-D.

Degree: 2019, Columbia University

 Predicting and understanding the dynamic of human motion has many applications such as motion synthesis, augmented reality, security, education, reinforcement learning, autonomous vehicles, and many… (more)

Subjects/Keywords: Computer science; Human mechanics; Human beings – Attitude and movement; Human activity recognition

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

APA (6th Edition):

Barsoum, E. (2019). Human Motion Anticipation and Recognition from RGB-D. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/d8-sq89-mm29

Chicago Manual of Style (16th Edition):

Barsoum, Emad. “Human Motion Anticipation and Recognition from RGB-D.” 2019. Doctoral Dissertation, Columbia University. Accessed November 28, 2020. https://doi.org/10.7916/d8-sq89-mm29.

MLA Handbook (7th Edition):

Barsoum, Emad. “Human Motion Anticipation and Recognition from RGB-D.” 2019. Web. 28 Nov 2020.

Vancouver:

Barsoum E. Human Motion Anticipation and Recognition from RGB-D. [Internet] [Doctoral dissertation]. Columbia University; 2019. [cited 2020 Nov 28]. Available from: https://doi.org/10.7916/d8-sq89-mm29.

Council of Science Editors:

Barsoum E. Human Motion Anticipation and Recognition from RGB-D. [Doctoral Dissertation]. Columbia University; 2019. Available from: https://doi.org/10.7916/d8-sq89-mm29


University of Illinois – Urbana-Champaign

17. Sorokin, Alexander. Expanding the limits of predictive methods: from supervised learning to novel sensors and massive human supervision.

Degree: PhD, 0112, 2012, University of Illinois – Urbana-Champaign

 The mission of machine learning is to empower computers to make generalizations from available data: labeled and unlabeled. The more labeled data we have the… (more)

Subjects/Keywords: crowdsourcing; computer vision; robotics; semi-supervised learning; object recognition; human activity recognition; sensor networks

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APA (6th Edition):

Sorokin, A. (2012). Expanding the limits of predictive methods: from supervised learning to novel sensors and massive human supervision. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/29722

Chicago Manual of Style (16th Edition):

Sorokin, Alexander. “Expanding the limits of predictive methods: from supervised learning to novel sensors and massive human supervision.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed November 28, 2020. http://hdl.handle.net/2142/29722.

MLA Handbook (7th Edition):

Sorokin, Alexander. “Expanding the limits of predictive methods: from supervised learning to novel sensors and massive human supervision.” 2012. Web. 28 Nov 2020.

Vancouver:

Sorokin A. Expanding the limits of predictive methods: from supervised learning to novel sensors and massive human supervision. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/2142/29722.

Council of Science Editors:

Sorokin A. Expanding the limits of predictive methods: from supervised learning to novel sensors and massive human supervision. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/29722


Universidade Nova

18. Santos, Diliana Maria Barradas Rebelo dos. Human activity recognition for an intelligent knee orthosis.

Degree: 2012, Universidade Nova

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Activity recognition with body-worn sensors is a large and growing field of research. In this… (more)

Subjects/Keywords: Biosignals; Human activity recognition; Signal-processing; Hidden Markov models

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APA (6th Edition):

Santos, D. M. B. R. d. (2012). Human activity recognition for an intelligent knee orthosis. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8493

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

Chicago Manual of Style (16th Edition):

Santos, Diliana Maria Barradas Rebelo dos. “Human activity recognition for an intelligent knee orthosis.” 2012. Thesis, Universidade Nova. Accessed November 28, 2020. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8493.

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

MLA Handbook (7th Edition):

Santos, Diliana Maria Barradas Rebelo dos. “Human activity recognition for an intelligent knee orthosis.” 2012. Web. 28 Nov 2020.

Vancouver:

Santos DMBRd. Human activity recognition for an intelligent knee orthosis. [Internet] [Thesis]. Universidade Nova; 2012. [cited 2020 Nov 28]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8493.

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

Council of Science Editors:

Santos DMBRd. Human activity recognition for an intelligent knee orthosis. [Thesis]. Universidade Nova; 2012. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/8493

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


University of Illinois – Chicago

19. Devanahalli Shashikumar, Sadgun Srinivas. 3D Human Activity Recognition by Indexing and Sequencing (RISq).

Degree: 2015, University of Illinois – Chicago

Human activity recognition has been a crucial area of research in computer vision over the past several years. Myriad applications of human activity recognition have… (more)

Subjects/Keywords: Image Processing; Computer Vision; Human Activity Recognition; RISq; Microsoft Kinect.

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APA (6th Edition):

Devanahalli Shashikumar, S. S. (2015). 3D Human Activity Recognition by Indexing and Sequencing (RISq). (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/19596

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

Chicago Manual of Style (16th Edition):

Devanahalli Shashikumar, Sadgun Srinivas. “3D Human Activity Recognition by Indexing and Sequencing (RISq).” 2015. Thesis, University of Illinois – Chicago. Accessed November 28, 2020. http://hdl.handle.net/10027/19596.

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

MLA Handbook (7th Edition):

Devanahalli Shashikumar, Sadgun Srinivas. “3D Human Activity Recognition by Indexing and Sequencing (RISq).” 2015. Web. 28 Nov 2020.

Vancouver:

Devanahalli Shashikumar SS. 3D Human Activity Recognition by Indexing and Sequencing (RISq). [Internet] [Thesis]. University of Illinois – Chicago; 2015. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10027/19596.

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

Council of Science Editors:

Devanahalli Shashikumar SS. 3D Human Activity Recognition by Indexing and Sequencing (RISq). [Thesis]. University of Illinois – Chicago; 2015. Available from: http://hdl.handle.net/10027/19596

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

20. Zhu, Yin. Activity recognition via social knowledge transfer.

Degree: 2014, Hong Kong University of Science and Technology

 People now live in a social world. The advent of online social networks not only connect us more tightly, but also enables us to record… (more)

Subjects/Keywords: Online social networks ; Data processing ; Human activity recognition

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APA (6th Edition):

Zhu, Y. (2014). Activity recognition via social knowledge transfer. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-70938 ; https://doi.org/10.14711/thesis-b1333757 ; http://repository.ust.hk/ir/bitstream/1783.1-70938/1/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 (16th Edition):

Zhu, Yin. “Activity recognition via social knowledge transfer.” 2014. Thesis, Hong Kong University of Science and Technology. Accessed November 28, 2020. http://repository.ust.hk/ir/Record/1783.1-70938 ; https://doi.org/10.14711/thesis-b1333757 ; http://repository.ust.hk/ir/bitstream/1783.1-70938/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Zhu, Yin. “Activity recognition via social knowledge transfer.” 2014. Web. 28 Nov 2020.

Vancouver:

Zhu Y. Activity recognition via social knowledge transfer. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2014. [cited 2020 Nov 28]. Available from: http://repository.ust.hk/ir/Record/1783.1-70938 ; https://doi.org/10.14711/thesis-b1333757 ; http://repository.ust.hk/ir/bitstream/1783.1-70938/1/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:

Zhu Y. Activity recognition via social knowledge transfer. [Thesis]. Hong Kong University of Science and Technology; 2014. Available from: http://repository.ust.hk/ir/Record/1783.1-70938 ; https://doi.org/10.14711/thesis-b1333757 ; http://repository.ust.hk/ir/bitstream/1783.1-70938/1/th_redirect.html

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

21. Zheng, Wenchen. Learning with limited data in sensor-based human behavior recognition.

Degree: 2011, Hong Kong University of Science and Technology

Human behavior recognition from sensor observations is an important topic in both artificial intelligence and mobile computing. It is also a difficult task as the… (more)

Subjects/Keywords: Human activity recognition ; Detectors  – Data processing ; Signal processing

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APA (6th Edition):

Zheng, W. (2011). Learning with limited data in sensor-based human behavior recognition. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-7320 ; https://doi.org/10.14711/thesis-b1155163 ; http://repository.ust.hk/ir/bitstream/1783.1-7320/1/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 (16th Edition):

Zheng, Wenchen. “Learning with limited data in sensor-based human behavior recognition.” 2011. Thesis, Hong Kong University of Science and Technology. Accessed November 28, 2020. http://repository.ust.hk/ir/Record/1783.1-7320 ; https://doi.org/10.14711/thesis-b1155163 ; http://repository.ust.hk/ir/bitstream/1783.1-7320/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Zheng, Wenchen. “Learning with limited data in sensor-based human behavior recognition.” 2011. Web. 28 Nov 2020.

Vancouver:

Zheng W. Learning with limited data in sensor-based human behavior recognition. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2011. [cited 2020 Nov 28]. Available from: http://repository.ust.hk/ir/Record/1783.1-7320 ; https://doi.org/10.14711/thesis-b1155163 ; http://repository.ust.hk/ir/bitstream/1783.1-7320/1/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:

Zheng W. Learning with limited data in sensor-based human behavior recognition. [Thesis]. Hong Kong University of Science and Technology; 2011. Available from: http://repository.ust.hk/ir/Record/1783.1-7320 ; https://doi.org/10.14711/thesis-b1155163 ; http://repository.ust.hk/ir/bitstream/1783.1-7320/1/th_redirect.html

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


Delft University of Technology

22. Friđriksdóttir, Esther (author). Human Activity Recognition using a Deep Learning Algorithm for Patient Monitoring.

Degree: 2019, Delft University of Technology

 Physical activity and mobility are important indicators of the recovery process of patients in the general ward of the hospital. Currently, monitoring mobility of hospitalized… (more)

Subjects/Keywords: Human Activity Recognition; Machine Learning; Deep Learning; Accelerometer; Classification; Patient Monitoring

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

APA (6th Edition):

Friđriksdóttir, E. (. (2019). Human Activity Recognition using a Deep Learning Algorithm for Patient Monitoring. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5028e05e-661f-4791-9d1f-e50d877aa24f

Chicago Manual of Style (16th Edition):

Friđriksdóttir, Esther (author). “Human Activity Recognition using a Deep Learning Algorithm for Patient Monitoring.” 2019. Masters Thesis, Delft University of Technology. Accessed November 28, 2020. http://resolver.tudelft.nl/uuid:5028e05e-661f-4791-9d1f-e50d877aa24f.

MLA Handbook (7th Edition):

Friđriksdóttir, Esther (author). “Human Activity Recognition using a Deep Learning Algorithm for Patient Monitoring.” 2019. Web. 28 Nov 2020.

Vancouver:

Friđriksdóttir E(. Human Activity Recognition using a Deep Learning Algorithm for Patient Monitoring. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 28]. Available from: http://resolver.tudelft.nl/uuid:5028e05e-661f-4791-9d1f-e50d877aa24f.

Council of Science Editors:

Friđriksdóttir E(. Human Activity Recognition using a Deep Learning Algorithm for Patient Monitoring. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:5028e05e-661f-4791-9d1f-e50d877aa24f

23. Abed, Sudad Hazem. Personalized smart residency with human activity recognition.

Degree: Thesis (M.S.), 2016, Ball State University

 This thesis proposes developing a smart sensor-based system that recognizes persons entering any room in a house and identifies them. After that, the system adjusts… (more)

Subjects/Keywords: Intelligent sensors.; Human activity recognition.; Dwellings  – Heating and ventilation  – Control.

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APA (6th Edition):

Abed, S. H. (2016). Personalized smart residency with human activity recognition. (Masters Thesis). Ball State University. Retrieved from http://cardinalscholar.bsu.edu/handle/123456789/200564

Chicago Manual of Style (16th Edition):

Abed, Sudad Hazem. “Personalized smart residency with human activity recognition.” 2016. Masters Thesis, Ball State University. Accessed November 28, 2020. http://cardinalscholar.bsu.edu/handle/123456789/200564.

MLA Handbook (7th Edition):

Abed, Sudad Hazem. “Personalized smart residency with human activity recognition.” 2016. Web. 28 Nov 2020.

Vancouver:

Abed SH. Personalized smart residency with human activity recognition. [Internet] [Masters thesis]. Ball State University; 2016. [cited 2020 Nov 28]. Available from: http://cardinalscholar.bsu.edu/handle/123456789/200564.

Council of Science Editors:

Abed SH. Personalized smart residency with human activity recognition. [Masters Thesis]. Ball State University; 2016. Available from: http://cardinalscholar.bsu.edu/handle/123456789/200564


Lehigh University

24. Li, Xin. Robust and Efficient Activity Recognition from Videos.

Degree: PhD, Computer Science, 2020, Lehigh University

  With technological advancement in embedded system design, powerful cameras have been embedded within smart phones, and wireless cameras can be easily deployed at street… (more)

Subjects/Keywords: Human activity recognition; Model decomposition; Trajectory prediction; Computer Sciences

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APA (6th Edition):

Li, X. (2020). Robust and Efficient Activity Recognition from Videos. (Doctoral Dissertation). Lehigh University. Retrieved from https://preserve.lehigh.edu/etd/5679

Chicago Manual of Style (16th Edition):

Li, Xin. “Robust and Efficient Activity Recognition from Videos.” 2020. Doctoral Dissertation, Lehigh University. Accessed November 28, 2020. https://preserve.lehigh.edu/etd/5679.

MLA Handbook (7th Edition):

Li, Xin. “Robust and Efficient Activity Recognition from Videos.” 2020. Web. 28 Nov 2020.

Vancouver:

Li X. Robust and Efficient Activity Recognition from Videos. [Internet] [Doctoral dissertation]. Lehigh University; 2020. [cited 2020 Nov 28]. Available from: https://preserve.lehigh.edu/etd/5679.

Council of Science Editors:

Li X. Robust and Efficient Activity Recognition from Videos. [Doctoral Dissertation]. Lehigh University; 2020. Available from: https://preserve.lehigh.edu/etd/5679


University of Waterloo

25. Miller, Nicholas. Detecting Hand-Ball Events in Video.

Degree: 2008, University of Waterloo

 We analyze videos in which a hand interacts with a basketball. In this work, we present a computational system which detects and classifies hand-ball events,… (more)

Subjects/Keywords: Machine Vision; Human Activity Recognition

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APA (6th Edition):

Miller, N. (2008). Detecting Hand-Ball Events in Video. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/3904

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

Chicago Manual of Style (16th Edition):

Miller, Nicholas. “Detecting Hand-Ball Events in Video.” 2008. Thesis, University of Waterloo. Accessed November 28, 2020. http://hdl.handle.net/10012/3904.

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

MLA Handbook (7th Edition):

Miller, Nicholas. “Detecting Hand-Ball Events in Video.” 2008. Web. 28 Nov 2020.

Vancouver:

Miller N. Detecting Hand-Ball Events in Video. [Internet] [Thesis]. University of Waterloo; 2008. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10012/3904.

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

Council of Science Editors:

Miller N. Detecting Hand-Ball Events in Video. [Thesis]. University of Waterloo; 2008. Available from: http://hdl.handle.net/10012/3904

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


University of New South Wales

26. Khalifa, Sara. Energy-efficient Human Activity Recognition for Self-powered Wearable Devices.

Degree: Computer Science & Engineering, 2016, University of New South Wales

 Advances in energy harvesting hardware have created an opportunity to realise self-powered wearables for continuous and pervasive Human Activity Recognition (HAR). Unfortunately, the power requirements… (more)

Subjects/Keywords: Self-powered Wearable Devices; Human Activity Recognition; Energy Harvesting; Energy Efficiency

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APA (6th Edition):

Khalifa, S. (2016). Energy-efficient Human Activity Recognition for Self-powered Wearable Devices. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/55849 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:39535/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Khalifa, Sara. “Energy-efficient Human Activity Recognition for Self-powered Wearable Devices.” 2016. Doctoral Dissertation, University of New South Wales. Accessed November 28, 2020. http://handle.unsw.edu.au/1959.4/55849 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:39535/SOURCE02?view=true.

MLA Handbook (7th Edition):

Khalifa, Sara. “Energy-efficient Human Activity Recognition for Self-powered Wearable Devices.” 2016. Web. 28 Nov 2020.

Vancouver:

Khalifa S. Energy-efficient Human Activity Recognition for Self-powered Wearable Devices. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2020 Nov 28]. Available from: http://handle.unsw.edu.au/1959.4/55849 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:39535/SOURCE02?view=true.

Council of Science Editors:

Khalifa S. Energy-efficient Human Activity Recognition for Self-powered Wearable Devices. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/55849 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:39535/SOURCE02?view=true

27. Xia, Lu, active 21st century. Recognizing human activity using RGBD data.

Degree: PhD, Electrical and Computer Engineering, 2014, University of Texas – Austin

 Traditional computer vision algorithms try to understand the world using visible light cameras. However, there are inherent limitations of this type of data source. First,… (more)

Subjects/Keywords: Activity recognition; RGBD; Depth sensing; 3D; Human detection; First-person; Human interaction

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

APA (6th Edition):

Xia, Lu, a. 2. c. (2014). Recognizing human activity using RGBD data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/24981

Chicago Manual of Style (16th Edition):

Xia, Lu, active 21st century. “Recognizing human activity using RGBD data.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed November 28, 2020. http://hdl.handle.net/2152/24981.

MLA Handbook (7th Edition):

Xia, Lu, active 21st century. “Recognizing human activity using RGBD data.” 2014. Web. 28 Nov 2020.

Vancouver:

Xia, Lu a2c. Recognizing human activity using RGBD data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/2152/24981.

Council of Science Editors:

Xia, Lu a2c. Recognizing human activity using RGBD data. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/24981

28. Boulahia, Said Yacine. Reconnaissance en-ligne d'actions 3D par l'analyse des trajectoires du squelette humain : Online 3D actions recognition by analyzing the trajectories of human's skeleton.

Degree: Docteur es, Informatique, 2018, Rennes, INSA

L'objectif de cette thèse est de concevoir une approche transparente originale apte à détecter en temps-réel l'occurrence d'une action, dans un flot non segmenté et… (more)

Subjects/Keywords: Trajectoires squeletiques; Gestes 3D; Reconnaissance en-ligne; Pattern recognition systems; Human activity recognition; Human-computer interaction; 006.4

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

APA (6th Edition):

Boulahia, S. Y. (2018). Reconnaissance en-ligne d'actions 3D par l'analyse des trajectoires du squelette humain : Online 3D actions recognition by analyzing the trajectories of human's skeleton. (Doctoral Dissertation). Rennes, INSA. Retrieved from http://www.theses.fr/2018ISAR0009

Chicago Manual of Style (16th Edition):

Boulahia, Said Yacine. “Reconnaissance en-ligne d'actions 3D par l'analyse des trajectoires du squelette humain : Online 3D actions recognition by analyzing the trajectories of human's skeleton.” 2018. Doctoral Dissertation, Rennes, INSA. Accessed November 28, 2020. http://www.theses.fr/2018ISAR0009.

MLA Handbook (7th Edition):

Boulahia, Said Yacine. “Reconnaissance en-ligne d'actions 3D par l'analyse des trajectoires du squelette humain : Online 3D actions recognition by analyzing the trajectories of human's skeleton.” 2018. Web. 28 Nov 2020.

Vancouver:

Boulahia SY. Reconnaissance en-ligne d'actions 3D par l'analyse des trajectoires du squelette humain : Online 3D actions recognition by analyzing the trajectories of human's skeleton. [Internet] [Doctoral dissertation]. Rennes, INSA; 2018. [cited 2020 Nov 28]. Available from: http://www.theses.fr/2018ISAR0009.

Council of Science Editors:

Boulahia SY. Reconnaissance en-ligne d'actions 3D par l'analyse des trajectoires du squelette humain : Online 3D actions recognition by analyzing the trajectories of human's skeleton. [Doctoral Dissertation]. Rennes, INSA; 2018. Available from: http://www.theses.fr/2018ISAR0009


University of Missouri – Kansas City

29. Vaka, Prakash Reddy. A Pervasive Middleware for Activity Recognition with Smartphones.

Degree: 2015, University of Missouri – Kansas City

Activity Recognition (AR) is an important research topic in pervasive computing. With the rapid increase in the use of pervasive devices, huge sensor data is… (more)

Subjects/Keywords: Human activity recognition; Smartphones; Thesis  – University of Missouri – Kansas City  – Computer science

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

APA (6th Edition):

Vaka, P. R. (2015). A Pervasive Middleware for Activity Recognition with Smartphones. (Thesis). University of Missouri – Kansas City. Retrieved from http://hdl.handle.net/10355/46659

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

Chicago Manual of Style (16th Edition):

Vaka, Prakash Reddy. “A Pervasive Middleware for Activity Recognition with Smartphones.” 2015. Thesis, University of Missouri – Kansas City. Accessed November 28, 2020. http://hdl.handle.net/10355/46659.

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

MLA Handbook (7th Edition):

Vaka, Prakash Reddy. “A Pervasive Middleware for Activity Recognition with Smartphones.” 2015. Web. 28 Nov 2020.

Vancouver:

Vaka PR. A Pervasive Middleware for Activity Recognition with Smartphones. [Internet] [Thesis]. University of Missouri – Kansas City; 2015. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10355/46659.

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

Council of Science Editors:

Vaka PR. A Pervasive Middleware for Activity Recognition with Smartphones. [Thesis]. University of Missouri – Kansas City; 2015. Available from: http://hdl.handle.net/10355/46659

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


San Jose State University

30. Narkhede, Anish Hemant. Human Activity Recognition Based on Multimodal Body Sensing.

Degree: MS, Computer Science, 2019, San Jose State University

  In the recent years, human activity recognition has been widely popularized by a lot of smartphone manufacturers and fitness tracking companies. It has allowed… (more)

Subjects/Keywords: Classification; dimensionality reduction; neural networks; human activity recognition; Artificial Intelligence and Robotics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Narkhede, A. H. (2019). Human Activity Recognition Based on Multimodal Body Sensing. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.zq8y-564m ; https://scholarworks.sjsu.edu/etd_projects/682

Chicago Manual of Style (16th Edition):

Narkhede, Anish Hemant. “Human Activity Recognition Based on Multimodal Body Sensing.” 2019. Masters Thesis, San Jose State University. Accessed November 28, 2020. https://doi.org/10.31979/etd.zq8y-564m ; https://scholarworks.sjsu.edu/etd_projects/682.

MLA Handbook (7th Edition):

Narkhede, Anish Hemant. “Human Activity Recognition Based on Multimodal Body Sensing.” 2019. Web. 28 Nov 2020.

Vancouver:

Narkhede AH. Human Activity Recognition Based on Multimodal Body Sensing. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2020 Nov 28]. Available from: https://doi.org/10.31979/etd.zq8y-564m ; https://scholarworks.sjsu.edu/etd_projects/682.

Council of Science Editors:

Narkhede AH. Human Activity Recognition Based on Multimodal Body Sensing. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.zq8y-564m ; https://scholarworks.sjsu.edu/etd_projects/682

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