Development and evaluation of the ARM algorithm: A novel approach to quantify musculoskeletal disorder risk factors in manual wheelchair users in the real world

This study introduces the ARM algorithm, which uses wearable sensors (IMUs) to track arm movements in manual wheelchair users. It helps assess the risk of shoulder problems by measuring repetitive motions and arm positions during daily activities. The algorithm was tested in different settings: community, in-home, and during free-living. The ARM algorithm accurately determined active and resting times. It also showed how arm usage differs between the dominant and non-dominant arms. The research demonstrates that the ARM algorithm can effectively monitor shoulder disorder risk factors in wheelchair users during their everyday routines, offering insights for developing preventative strategies.

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  The ARM algorithm accurately estimated active and resting times (>98%) in the community, validated against video analysis.
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  The study confirmed asymmetries in arm usage, with the dominant arm being more active and the non-dominant arm having longer resting periods in free-living environments.
• 3
  Analysis revealed that active bouts longer than 10 seconds showed higher total time, average duration, and number of movement cycles for the dominant arm.