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Towards Understanding the Relation Between Gait, Built-environment, and Real-life Mobility of Older Adults via Accelerometry and Global Positioning System-based Wearable Technology

Suri, Anisha (2023) Towards Understanding the Relation Between Gait, Built-environment, and Real-life Mobility of Older Adults via Accelerometry and Global Positioning System-based Wearable Technology. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Mobility limitations affect approximately 30 million older adults in United States. One in three older adults experience a fall annually. Mobility related disability is known to reduce participation in the community and leads to a reduced quality of life. These alarming trends require understanding of mobility, which is a multi-factorial concept. Beyond an individual’s physical capacity, their ability to walk efficiently can impact mobility behaviors in real-world. Besides, growing evidence suggests cognition and psycho-social factors also act as facilitators and barriers to mobility. Walking or gait is a highly complex daily-activity, most instrumental in driving our day-to-day active mobility. Quantifying ’how we walk’ via laboratory-measured
gait patterns is of interest to clinicians and researchers. The relation of gait performance measures to outcomes such as real-life mobility, cognition fear of falling, and neighborhood walkability characteristics could help in identifying individuals at greater risk of developing
gait, cognitive, and psychosocial disability, and further inform intervention strategies. In this research, we utilize advanced signal processing techniques with sensor technology in quantifying ’quality of walking’ and ’daily-life mobility’. In addition to statistical methods,
we use supervised and unsupervised machine learning approaches to identify patterns in gait response when individuals are exposed to walking tasks. Spatio-temporal mobility in natural environments is quantified using actigraphy and global positioning system. Like never before,
utilizing signal processing and machine learning in understanding gait and mobility can help in identifying risk-factors, ultimately delaying disability, for an independent healthy aging.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Suri, Anishaans368@pitt.eduans3680000-0001-5308-2079
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSejdic, Ervinesejdic@pitt.eduesejdic
Committee MemberRosso, Andrea Lalr143@pitt.edualr143
Committee MemberAkcakaya, Muratakcakaya@pitt.eduakcakaya
Committee MemberDallal, Ahmedahd12@pitt.eduahd12
Committee MemberZhan, Liangliang.zhan@pitt.eduliang.zhan
Date: 19 January 2023
Date Type: Publication
Defense Date: 10 November 2022
Approval Date: 19 January 2023
Submission Date: 31 October 2022
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 189
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Aging, Physical Activity, Gait quality, Digital health, Machine learning
Date Deposited: 19 Jan 2023 19:16
Last Modified: 19 Jan 2023 19:16


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