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Physical Activity Monitoring System for Manual Wheelchair Users

Hiremath, Shivayogi Vishwanath (2013) Physical Activity Monitoring System for Manual Wheelchair Users. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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People with disabilities who rely on manual wheelchairs as their primary means of mobility face daily challenges such as mobility limitations and environmental barriers when engaging in regular physical activity. Therefore, our research addressed the need for a valid and reliable physical activity monitor to assess and quantify physical activities among manual wheelchair users (MWUs) in free-living environments. Providing an accurate estimate of physical activity (PA) levels in MWUs can assist researchers and clinicians to quantify day-to-day PA levels,
leading to recommendations for a healthier lifestyle. In the first stage we developed and evaluated new classification and EE estimation models for MWUs with spinal cord injury (N=45) using SenseWear, an off-the-shelf activity monitor, designed for the general population without disabilities. The results suggested that SenseWear can be used by researchers and clinicians to detect and estimate the EE for four activities tested in our study. The second phase of our research project developed an activity monitor especially designed for MWUs. Previous
research in community participation of MWUs and the studies discussed above found that wheelchair mobility characteristics are necessary to study PA patterns in MWUs. This requirement led us to develop and evaluate a Physical Activity Monitor System (PAMS) composed of two components: a gyroscope based wheel rotation monitor (G-WRM for tracking
wheelchair mobility and an accelerometer that quantifies upper arm movement. We tested PAMS in 45 MWUs with SCI in the structured (laboratory) and semi-structured environments (National Veterans Wheelchair Gamers 2012). In addition, we also tested a subsection of this population (N=20) a second time, in their home environments. The PAs were classified as resting, armergometry, other sedentary activities, activities involving some wheelchair movement,
propulsion, basketball and caretaker pushing. The EE estimation results (error: -9.8%) and the classification results (accuracy: 89.3%) indicate that PAMS can reliably track wheelchair-based activities in laboratory and home environments. Furthermore, we used participatory action
design to evaluate the usability of PAMS in six MWUs with SCI. The usability study indicated that users were very satisfied with PAMS and the information provided by the smartphone to the users about their PA levels.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Hiremath, Shivayogi Vishwanathsvh4@pitt.eduSVH4
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDing, Dandad5@pitt.eduDAD5
Committee MemberCooper, Rory Arcooper@pitt.eduRCOOPER
Committee MemberDicianno, Brad Edicianno@pitt.eduDICIANNO
Committee MemberIntille,
Committee MemberJonathan,
Date: 12 September 2013
Date Type: Publication
Defense Date: 11 July 2013
Approval Date: 12 September 2013
Submission Date: 17 July 2013
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages: 233
Institution: University of Pittsburgh
Schools and Programs: School of Health and Rehabilitation Sciences > Rehabilitation Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Spinal Cord Injury, Wheelchair Users, Physical Activity, Machine Learning, Multi-sensor Activity Monitor, Classification, Regression, Energy Expenditure, Smartphone, Usability Testing
Date Deposited: 12 Sep 2013 15:05
Last Modified: 12 Sep 2018 05:15


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