Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

How Well Can Wearable Devices Measure Physical Activity in Manual Wheelchair Users with Spinal Cord Injury

Veerubhotla, Akhila (2019) How Well Can Wearable Devices Measure Physical Activity in Manual Wheelchair Users with Spinal Cord Injury. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img] PDF
Restricted to University of Pittsburgh users only until 16 September 2024.

Download (2MB) | Request a Copy


Manual wheelchair users (MWUs) with spinal cord injury (SCI) are at the lower end of the physical activity (PA) spectrum. Physical inactivity combined with physiological changes after SCI puts them at an increased risk for secondary health conditions such as cardiometabolic and cardiorespiratory diseases. Increased PA and decreased sedentary time have been shown to contribute to the primary and secondary prevention of chronic health conditions. The objective measurement of habitual PA is an important part of health promoting efforts to address physical inactivity. While wearable devices are believed to be best suited for objective measurement of PA in everyday life and are widely used in the ambulatory population, most commercially available devices do not cater to people with SCI. Currently there are limited validated tools that can provide comprehensive PA measures relevant to this population. This dissertation developed and evaluated custom algorithms for assessing activity intensities and wheelchair propulsion characteristics using off-the-shelf wearable devices in people with SCI. The data collection included a wide range of activities of daily living and exercises from lab-based structured protocols to a one-week community-based trial. Both traditional statistical modeling and machine learning techniques were used to develop the custom algorithms, and a rigorous procedure was followed to evaluate these algorithms, which includes not only iterative cross-validation, but also out-of-sample validation where a separate data set from the training data was used to evaluate the algorithm performance. The developed algorithms along with wearable devices from this dissertation could help researchers fill a number of voids in health and PA research targeting people with SCI and other mobility impairments.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Veerubhotla, Akhilaakhila.veerubhotla@gmail.comalv47
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDing, Dandad5@pitt.edudad5
Committee MemberBoninger, Michaelboninger@pitt.eduboninger
Committee MemberTerhost, Laurenlat15@pitt.edulat15
Committee MemberZhou, Leminglzhou1@pitt.edulzhou
Committee MemberRice,
Date: 16 September 2019
Date Type: Publication
Defense Date: 22 July 2019
Approval Date: 16 September 2019
Submission Date: 13 September 2019
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages: 119
Institution: University of Pittsburgh
Schools and Programs: School of Health and Rehabilitation Sciences > Rehabilitation Science and Technology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: machine learning, signal processing, algorithm, manual wheelchair users, activity monitoring, physical activity, exercise, measurement
Date Deposited: 26 Aug 2020 12:43
Last Modified: 26 Aug 2020 12:43


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item