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Quantitative Assessment of Parkinson Patient’s Health Improvement Using Kinect for Telerehabilitation

Alavian, Mostafa | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52663 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Behzadipour, Saeed; Taghizade, Ghorban
  7. Abstract:
  8. Parkinson's disease is the most common progressive neurological disorder after Alzheimer, which is associated with motor disabilities. One of the most effective ways to improve patient’s condition with this disease, is rehabilitation.Assessment of the patient during rehabilitation is very important in order to give a better understanding of patient's status to the specialist, but qualitative assessment methods in traditional rehabilitation are ineffective in fast and accurate evaluation of the patient's condition; and because of their need for patient’s attendance in clinic, they will incur huge medical costs. Therefore, the purpose of the present study is to present a quantitative method to assess patients' status and to see the process of his recovery.To achieve this, a rich set of upper extremity movement patterns were selected and designed in the virtual reality. Then, the Kinect camera captured the patients' upper extremity movements while they were performing these patterns, in two steps; and these data were used to calculate biomechanical indices, which are the measures of movement quality. In the first step, data were collected from 40 patients in 3 sessions. The purpose of this step was to study the reliability of the biomechanical indices in the literature and their validity in comparison with conventional clinical ones. In the second step, data were collected from 14 patients in 24 treatment sessions. The purpose of this section was to study the responsiveness of these indices.In order to achieve a faster and more accurate assessment, the validity, reliability and responsiveness of these indices were evaluated by statistical methods. The results of the initial step showed that the velocity peaks index and indices related to the jerk did not have a normal distribution, but all the indices with normal distribution were reliable and valid due to their significant correlation with the results of clinical indices. Finally, it was found that only the 3 indices (mean peak velocity, mean velocity and maximum velocity) in the reach movement tasks, and the 1 index (Spark) in the track movements, were able to separate the medical status of patients.The results of the second step showed that among these 4 indices, the mean velocity peaks and mean velocity in the reaching tasks of both sides, plus in tracking tasks of more affected side, and the Spark only in tracking tasks in both sides has a good responsiveness; Trends of these indices were also drawn during the treatment, which was found that these indices can show a recovery process with little tolerance. Finally, it was found that the averages of velocity peaks and mean velocity in reaching tasks and Spark in tracking tasks, all for more affected hand, had the fastest recovery time, especially Spark index; Therefore they can be used as the assessment metrics of the telerehabilitation system designed in this study
  9. Keywords:
  10. Tele-rehabilitation ; Parkinson Disease ; Movement Assesment ; Kinect Sensor ; Upper Limb ; Kinematic Biomechanical Indices

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