A hybrid sensory system for upper extremity stroke rehabilitation assessment

Post-stroke rehabilitation is essential for promoting patient's recovery and restoring their independence. Despite their significance, the current assessment methods for post-stroke rehabilitation have seen limited advancements, remaining predominantly reliant on clinicians' observations and experience. This reliance leads to a lack of intelligent, portable, and universal applicability of these methods, thereby hindering quantitative assessment and effective rehabilitation outcomes. Given recent advancements in sensing technology, these conventional methods require substantial improvement or replacement. In this study, a hybrid sensory system that integrates a triboelectric sensor, and a piezoelectric sensor is developed to monitor the flexion angle and flexion force of human joints. The hybrid system exhibits a high sensitivity of 83.1 mV/degree for flexion angle and 3.7 N/degree for flexion force in the range of 15° to 90° and 52 N to 297 N, respectively. By incorporating a six-component metric assessment (MA) method, the system provides an objective evaluation of stroke patients' recovery progress, and features excellent repeatability, linearity, accuracy, durability over 4000 cycles, and minimal hysteresis. Additionally, a user-friendly human-machine interface (HMI) is developed to seamlessly connect the data acquisition system with end users. The system's feasibility and universal applicability are validated through a control study involving 30 healthy subjects, while its efficacy is further confirmed on two stroke patients. The results demonstrate the potential of the integrated system as a robust and precise tool for quantitative assessment in post-stroke rehabilitation. The sensor system can also be used to monitor other joints, such as wrist and elbow, hand gestures, and gait profile, making it as a promising solution for precise, quantitative, and intelligent assessment of motor function recovery.