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DTSTAMP:20251016T135124Z
LOCATION:Riverside East
DTSTART;TZID=America/Chicago:20251015T173000
DTEND;TZID=America/Chicago:20251015T183000
UID:HFESAM_ASPIRE 2025_sess138_POST386@linklings.com
SUMMARY:Human Factors Principles Enhance the Design of a Machine Learning-
 Based Lower-Limb Exercise System for Older Adults with Knee Pain
DESCRIPTION:Tianrong Chen, Hao Liu, and Calvin Kalun Or (The University of
  Hong Kong)\n\nKnee pain in older adults significantly reduces their physi
 cal function and quality of life. While lower-limb exercises are effective
  for treating this health issue, individuals’ access to supervised program
 s is often limited. To address this, our research team developed a machine
  learning-based lower-limb exercise system that offers video demonstration
 s of exercises, provides real-time movement feedback, and tracks performan
 ce and progress. Initial evaluation of the system revealed usability barri
 ers. Consequently, we applied human factors principles to improve the syst
 em design and subsequently evaluated the usability and acceptance of the e
 nhanced system. Ten adults (aged 60-73) with knee pain used the system to 
 perform lower-limb exercises. Their opinions on usability and acceptance w
 ere gathered. We found that the enhanced system was perceived as both usab
 le and acceptable. These findings underscore the necessity of integrating 
 human factors principles into the design of such systems, as this approach
  leads to systems with a high likelihood of user acceptance. Furthermore, 
 by incorporating human factors insights into the design of digital health 
 applications, developers can create intuitive user interfaces that align w
 ith users’ preferences and expectations.\n\n
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