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You searched for subject:(Kinematic Features). Showing records 1 – 2 of 2 total matches.

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RMIT University

1. Zham, P. Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease.

Degree: 2019, RMIT University

Parkinson's disease is one of the most prevalent neurodegenerative conditions. Currently, there is no standard clinical tool available to diagnose PD. One of the research priorities is to come up with biomarkers which will improve the diagnostic process and can be used for the clinical test. At present, the only way to assess this disease is by visually observing the symptoms of the patient which is performed only by expert neurologists. As of now, there is no treatment to prevent the progression of PD. However, there is an elemental drug `Levodopa' (L-dopa) available to control the disease by increasing dopamine cells in the brain. It is important to detect PD and start treatment in the early stages as it helps to control the symptoms and significantly delays the development of motor complications. In this study fine motor symptoms handwriting has been studied. As a first objective I have conducted the experiments on the significant number of patients and age-matched control (112 Participants:56 PD and 56 controls), and thus completed the task of data collection. The system developed extracts the dynamic features of the handwriting/drawing, reports the possible strength of dynamic features providing a basis for automated analysis. The advantage of this approach is that patients are not required to follow complex commands, and the analysis can be fully automized. I anticipate that following appropriate clinical tests already planned, the system will be able to detect early disease symptoms remotely outside hospitals or clinics. It could also be used for self-evaluation by patients with neuromuscular and motor neuron disorders. This device can be used without compromising on the comfort level of Patients who may still prefer writing with an ink pen on plain paper. This study proposes a new feature `Composite Index of Speed and Pen-pressure' (CISP) to distinguish between different stages of Parkinson's disease. The experiment also demonstrated a method which can be used with guided spiral drawing to improve classification results to predict Parkinson's disease. Further, I recommend using a panel of writing tasks which might prove to be an effective biomarker for cell loss in the substantia nigra and the associated dopamine deficiency. Thus, models developed can be used in designing an automated application for predicting and monitoring Parkinson's disease

Subjects/Keywords: Fields of Research; Parkinson'; s disease; Levodopa; Dynamic features; Kinematic features; Machine learning; Micrographia

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

APA (6th Edition):

Zham, P. (2019). Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:162840

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):

Zham, P. “Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease.” 2019. Thesis, RMIT University. Accessed December 13, 2019. http://researchbank.rmit.edu.au/view/rmit:162840.

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

MLA Handbook (7th Edition):

Zham, P. “Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease.” 2019. Web. 13 Dec 2019.

Vancouver:

Zham P. Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease. [Internet] [Thesis]. RMIT University; 2019. [cited 2019 Dec 13]. Available from: http://researchbank.rmit.edu.au/view/rmit:162840.

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

Council of Science Editors:

Zham P. Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease. [Thesis]. RMIT University; 2019. Available from: http://researchbank.rmit.edu.au/view/rmit:162840

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


University of Illinois – Urbana-Champaign

2. Bao, Ruxiao. Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features.

Degree: MS, Civil Engineering, 2015, University of Illinois – Urbana-Champaign

This thesis presents a fast and scalable method for activity analysis of construction equipment involved in earthmoving operations from highly varying long-sequence videos obtained from fixed cameras. A common approach to characterize equipment activities consists of detecting and tracking the equipment within the video volume, recognizing interest points and describing them locally, followed by a bag-of-words representation for classifying activities. While successful results have been achieved in each aspect of detection, tracking, and activity recognition, the highly varying degree of intra-class variability in resources, occlusions and scene clutter, the difficulties in defining visually-distinct activities, together with long computational time have challenged scalability of current solutions. In this thesis, we present a new end-to-end automated method to recognize the equipment activities by simultaneously detecting and tracking features, and characterizing the spatial kinematics of features via a decision tree. The method is tested on an unprecedented dataset of 5hr-long real-world videos of interacting pairs of excavators and trucks. The Experimental results show that the method is capable of activity recognition with accuracy of 88.91% with a computational time less than 1- to-1 ratio for each video length. The benefits of the proposed method for root-cause assessment of performance deviations are discussed. Advisors/Committee Members: Golparvar Fard, Mani (advisor).

Subjects/Keywords: Kinematic Features; Activity Recognition; Convolutional Neural Network; Construction Equipment

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

APA (6th Edition):

Bao, R. (2015). Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/89233

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):

Bao, Ruxiao. “Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed December 13, 2019. http://hdl.handle.net/2142/89233.

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

MLA Handbook (7th Edition):

Bao, Ruxiao. “Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features.” 2015. Web. 13 Dec 2019.

Vancouver:

Bao R. Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Dec 13]. Available from: http://hdl.handle.net/2142/89233.

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

Council of Science Editors:

Bao R. Characterizing construction equipment activities in long video sequences of earthmoving operations via kinematic features. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/89233

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

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