Enhancing Upper Limb Rehabilitation With fNIRS-Measured Brain Activity During Human–Robot Interaction

Abstract Robotics and machine learning algorithms can potentially enhance upper limb rehabilitation, addressing the limitations of traditional therapy methods. This study presents a novel Human–Robot Interaction (HRI) platform with human brain activities assessment capability aimed at enhancing upper limb rehabilitation by addressing the limitations of conventional therapy. Utilizing a 7DOF Franka Emika robotic arm, the system supports patients in performing lifting, grasping, and reaching tasks structured based on Wolf Motor Function Test (WMFT). Functional near-infrared spectroscopy (fNIRS) concurrently monitors cortical activation and functional connectivity to evaluate neural engagement and recovery. Visual feedback guides participants, while forearm electromyography (EMG) and brain activity from the moving limb are recorded to train deep learning models that classify physiological movement and cognitive load in real-time. Quantitative performance metrics, including average trajectory deviation and nondimensional squared jerk, assess movement accuracy and smoothness, correlating with task complexity. The platform also incorporates a robot impedance control scheme and an interactive interface to adapt assistance dynamically based on predicted movement. By integrating biomechanical performance data with neural indicators, this approach enables a personalized, data-driven rehabilitation framework.