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Deep Learning Approach to Skeletal Performance Evaluation of Physical Therapy Exercises.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning Approach to Skeletal Performance Evaluation of Physical Therapy Exercises./
作者:	Garg, Bhanu.
面頁冊數:	1 online resource (33 pages)
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30523402click for full text (PQDT)
ISBN:	9798379918309

Deep Learning Approach to Skeletal Performance Evaluation of Physical Therapy Exercises.
Garg, Bhanu.

Deep Learning Approach to Skeletal Performance Evaluation of Physical Therapy Exercises. - 1 online resource (33 pages)

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.S.)--University of California, San Diego, 2023.

Includes bibliographical references

At-home exercising strongly predicts physical therapy patient outcomes, underscoring the need for analyzing patient behaviors at-home via remote patient monitoring. Contemporary methods for remote patient monitoring rely on specialized sensors, i.e., Inertial Measurement Units, RGB-Depth cameras, motion capture systems, or stereo vision which are costly and not scalable to all physical therapy patients. Here, we observe a lack of literature using only a monocular RGB camera. In this paper, we demonstrate a skeletal feedback model for at-home exercises using only video acquired from a smartphone camera. We propose models for (i) Patient Performance Evaluation - which classifies the correctness of exercises, and (ii) Guidance - which identifies why the exercise went wrong so the patient can correct themselves. We use these models on our dataset of four common physical therapy exercises labeled by a physical therapist. Our results demonstrate the feasibility of using skeletal data from state-of-the-art 3D human pose estimation models for physical rehabilitation exercise evaluation and guidance. Thus, we enable remote patient monitoring and guidance from a single camera - making it highly cost-effective and scalable.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379918309Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

542853
Electronic books.

Deep Learning Approach to Skeletal Performance Evaluation of Physical Therapy Exercises.
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