Gait Analysis and Rehabilitation Robotics: A Hybrid Approach using Wearable Sensors and Reinforcement Learning

Abstract

The integration of wearable sensor technologies with advanced machine learning algorithms has catalyzed a paradigm shift in the domain of gait analysis and rehabilitation robotics. Traditional rehabilitation strategies rely heavily on clinician-led interventions and subjective assessment, which can limit precision and adaptability in patient care. This study proposes a hybrid framework combining wearable sensor-based gait monitoring with reinforcement learning (RL)-driven rehabilitation robotics. The framework leverages real-time kinematic and kinetic data from wearable inertial measurement units (IMUs) and force sensors to dynamically adapt robotic assistance, optimizing gait restoration protocols for patients with neuromuscular impairments. The article provides a comprehensive analysis of sensor modalities, data acquisition pipelines, reinforcement learning algorithms, and human-in-the-loop optimization strategies. Additionally, it addresses explainability, safety, and interpretability of the system, ensuring clinical relevance and regulatory compliance. Results from simulations and pilot studies demonstrate improved gait symmetry, stability, and motor learning efficiency compared to conventional robotic therapy approaches. This work underscores the transformative potential of hybrid sensor-RL frameworks for precision rehabilitation and personalized mobility restoration.