Jon Pearlman, Ph.D. | University of Pittsburg, UCP Wheels for Humanity; Anand Mhatre, Ph.D. | Department of Rehabilitation Science & Technology, University of Pittsburgh; Perth Rosen, MS | UCP Wheels for Humanity
Competition Sponsor: National Institute on Aging, National Institutes of Health
Awardee Year: 2020
Elderly wheelchair users experience wheelchair breakdowns every 2-3 months in low- and middle-income countries (LMICs) and rural areas of high-income countries. One in three breakdowns leads to adverse physical, social, psychosocial and economic consequences to wheelchair users which increases the public health and personal burden. Preventative wheelchair maintenance has been found to reduce the frequency of wheelchair breakdowns by ten-fold, but compliance with maintenance recommendations is extremely low because they are generic and not reflective of how and where the wheelchair is being used. To address this issue, we are developing a low-cost, scalable maintenance application that leverages artificial intelligence tools to provide maintenance recommendations tailored to how a wheelchair is used. The availability of low-cost technology and widespread use of smartphones by the elderly and people with disabilities in LMICs has led us to develop a smartphone application called WheelTrak that measures wheelchair wear as a function of usage in community. Based on the wear factors, the application produces a Wheelchair Wear Index (WWI) that is representative of wear of critical wheelchair parts that are prone to breakdown. Once a WWI threshold is reached, maintenance is required, and the application notifies the user and/or caregiver who can conduct maintenance to avoid breakdowns and related health consequences. We will conduct a data collection study in collaboration with our wheelchair industry partner – UCP Wheels in El Salvador – and characterize the WWI for the elderly by tracking wear factors which include user’s travel distance, ground shocks and surface vibrations using WheelTrak and a wheel sensor. Based on the trained WWI algorithm, a preventative maintenance schedule will be developed for older adults that can be employed through WheelTrak for maintenance reminders. Semi-structured interviews will be conducted to evaluate the usability of the application and gather barriers to maintenance. User feedback will assist us in improving WheelTrak for greater user satisfaction and compliance with maintenance and addressing any personal or logistical challenges that elderly users and their caregivers or family members may face with conducting maintenance activities in LMICs. Findings from the proposed studies in this application will assist us in planning future studies to investigate the WWI-enabled WheelTrak tool as an intervention to prevent or reduce breakdowns and health consequences with the elderly in LMICs.
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