PulseRide: A Robotic Wheelchair for Personalized Exertion Control with Human-in-the-Loop Reinforcement Learning
Azizul Zahid, Bibek Poudel, Danny Scott, Jason Scott, Scott Crouter, Weizi Li, Sai Swaminathan

TL;DR
PulseRide is a personalized robotic wheelchair system that uses human-in-the-loop reinforcement learning to adapt assistance based on physiological data, effectively maintaining users' exertion levels and reducing fatigue.
Contribution
It introduces a novel adaptive wheelchair system that integrates real-time physiological monitoring with reinforcement learning for personalized exertion control.
Findings
Maintains heart rate within moderate activity zone 71.7% longer than manual wheelchairs.
Reduces muscle contractions by 41.86%, delaying fatigue.
Enhances user comfort and engagement through adaptive assistance.
Abstract
Maintaining an active lifestyle is vital for quality of life, yet challenging for wheelchair users. For instance, powered wheelchairs face increasing risks of obesity and deconditioning due to inactivity. Conversely, manual wheelchair users, who propel the wheelchair by pushing the wheelchair's handrims, often face upper extremity injuries from repetitive motions. These challenges underscore the need for a mobility system that promotes activity while minimizing injury risk. Maintaining optimal exertion during wheelchair use enhances health benefits and engagement, yet the variations in individual physiological responses complicate exertion optimization. To address this, we introduce PulseRide, a novel wheelchair system that provides personalized assistance based on each user's physiological responses, helping them maintain their physical exertion goals. Unlike conventional assistive…
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Taxonomy
TopicsSpinal Cord Injury Research · Gaze Tracking and Assistive Technology · Prosthetics and Rehabilitation Robotics
