東華大學圖書館 |

Digital Tools to Enable Large-Scale Access to Biomechanical Assessment.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Digital Tools to Enable Large-Scale Access to Biomechanical Assessment./
作者:	Boswell, Melissa Ann.
面頁冊數:	1 online resource (185 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Teaching. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29224483click for full text (PQDT)
ISBN:	9798841529385

Digital Tools to Enable Large-Scale Access to Biomechanical Assessment.
Boswell, Melissa Ann.

Digital Tools to Enable Large-Scale Access to Biomechanical Assessment. - 1 online resource (185 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--Stanford University, 2022.

Includes bibliographical references

How we move and how much we move profoundly affect our health and wellbeing. In the field of biomechanics, we analyze movement to help identify, monitor, and treat diseases and disorders that impact movement. However, biomechanical intervention and monitoring have historically been restricted to a laboratory setting. This dissertation proposes an interdisciplinary approach to improving accessibility to biomechanical intervention and monitoring through bridging advancements in computer science, biomechanics, and psychology. Through three studies, I describe the development of new machine learning methods, mobile biomechanical tools, and digital psychological intervention to improve access to biomechanical assessments.In the first study, we developed a machine learning model to predict a key biomechanical measure of osteoarthritis progression. Altering the foot progression angle is a gait modification that aims to reduce the knee adduction moment, a surrogate measure of internal knee loading, to improve pain and slow disease progression in individuals with knee osteoarthritis. Foot progression angle modifications must be personalized to an individual, which traditionally requires an expensive gait laboratory. In this study, I demonstrate the feasibility of identifying personalized biomechanical interventions with a smartphone camera. We developed a machine learning model to predict the peak knee adduction moment using anatomical landmark positions identifiable with two-dimensional video. The model was 92% accurate in predicting changes in the peak knee adduction moment with foot progression angle modification, suggesting that it is feasible to use this tool with smartphone camera videos to prescribe personalized gait modifications in clinical settings.If made accessible for use with only a smartphone, pose estimation algorithms that predict joint locations from two-dimensional video have the potential to be used for at-home biomechanical self-assessment. In the second study, we tested the clinical relevance of pose estimation from at-home smartphone videos with the five-repetition sitto-stand test (5STS), one of the most widely used tests for measuring an individual's physical functioning. We created a web app that gave participants instructions to upload and record an at-home video of the 5STS, which was automatically processed to extract 5STS timing and kinematics. After remotely collecting data from over 350 participants, we evaluated the relationships between automatically-extracted 5STS timing and kinematics and self-reported demographic and health data. We found that longer times to complete the 5STS test were associated with older age, higher BMI, and lower physical health score. A larger maximum trunk flexion angle was associated with the presence of osteoarthritis. Trunk flexion angle remained a predictor of osteoarthritis presence (p=0.037) even when controlling for age, sex, and BMI. Our large dataset enabled exploratory analyses that found associations between trunk flexion angle and ethnicity and greater trunk acceleration and higher mental health score. The consistency of our results with lab-based studies supports the use of our open-source web app by researchers and clinicians to leverage biomechanics for at-home monitoring of physical functioning.Increasing access to movement-based interventions does not guarantee that individuals will use them or increase their activity levels because of them. Exercise is important for improving knee pain and functioning in individuals with osteoarthritis. However, exercise participation and adherence to physical activity interventions are low in the osteoarthritis population.

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

Mode of access: World Wide Web

ISBN: 9798841529385Subjects--Topical Terms:

517098
Teaching.
Index Terms--Genre/Form:

542853
Electronic books.

Digital Tools to Enable Large-Scale Access to Biomechanical Assessment.
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504 $a Includes bibliographical references
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