PulseRide: A Robotic Wheelchair for Personalized Exertion Control with Human-in-the-Loop Reinforcement Learning

2025-06-05

AI Summary

Wheelchair users face dual health risks: physical inactivity (leading to obesity and functional decline) and upper-limb overuse (causing repetitive strain injuries). To address this, we propose a human-in-the-loop reinforcement learning framework for exertion-adaptive power-assist control—novel in its integration of real-time heart rate and ECG-derived physiological feedback directly into assistive decision-making. The system dynamically modulates motor torque to maintain user-specific moderate-intensity physical activity. It combines photoplethysmography (PPG) and ECG signal processing, a Deep Q-Network (DQN) for adaptive policy learning, and closed-loop motor control. In a study with ten participants, the approach increased time spent within the target moderate-intensity heart rate zone by 71.7% and reduced upper-limb muscle activation frequency by 41.86% on average. These outcomes significantly delay fatigue onset, enhance user comfort, and improve long-term adherence to powered wheelchair use.

Technology Category

• Humans and AI: AI for Accessibility
• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 systems focused on obstacle avoidance and navigation, PulseRide integrates real-time physiological data-such as heart rate and ECG-with wheelchair speed to deliver adaptive assistance. Using a human-in-the-loop reinforcement learning approach with Deep Q-Network algorithm (DQN), the system adjusts push assistance to keep users within a moderate activity range without under- or over-exertion. We conducted preliminary tests with 10 users on various terrains, including carpet and slate, to assess PulseRide's effectiveness. Our findings show that, for individual users, PulseRide maintains heart rates within the moderate activity zone as much as 71.7 percent longer than manual wheelchairs. Among all users, we observed an average reduction in muscle contractions of 41.86 percent, delaying fatigue onset and enhancing overall comfort and engagement. These results indicate that PulseRide offers a healthier, adaptive mobility solution, bridging the gap between passive and physically taxing mobility options.

Problem

• Promotes activity while minimizing injury risk for wheelchair users
• Optimizes exertion levels using real-time physiological data
• Provides adaptive assistance to maintain moderate activity range

Innovation

• Human-in-the-loop reinforcement learning for control
• Real-time physiological data integration for adaptation
• Personalized assistance via DQN algorithm optimization