Leveraging Multimodal Monitoring in Plan-Based Robot-Aided Rehabilitation

Robotic rehabilitation systems typically rely on kinematic data to assess exercise execution, focusing on pose accuracy without evaluating the physical impact on the patient. This paper explores the integration of physiological monitoring into an automated planning system for robot-aided rehabilitation. Specifically, we assess whether Heart Rate (HR) can provide meaningful feedback on the experimented physical workload while executing physical tasks. Ten healthy participants completed a session of 10 exercises, with cumulative exercise intensity (ΣI) and HR recorded. A Pearson correlation analysis revealed an average strong correlation of 0.63 across all participants, indicating that HR reliably reflects physical effort. The results suggest that incorporating HR feedback allows the system to dynamically adjust exercise intensity in real-time, ensuring that patients are neither over- nor under-challenged.