Extracting Human-Exoskeleton Interaction Torque for Cable-Driven Upper-Limb Exoskeleton Equipped With Torque Sensors

Powered exoskeletons have global trends in broad applications, such as rehabilitation and human strength amplification in industry, military, and activities of daily livings. The motion intention of the exoskeleton wearer can be obtained using the interaction force at the physical human-machine interface. This article implements joint torque sensors in a custom-made cable-driven exoskeleton. The model of the torque sensor signal is established to extract the human-exoskeleton interaction (HEI) torque, which can be used to predict the human upper-limb motion intention. To accurately decouple the HEI torque from other components in the torque sensor signal, a nonlinear numerical friction model composed of the cable and joint parts is investigated based on the LuGre friction model. A protocol for parameter identification of the proposed friction model is verified experimentally. Furthermore, a coefficient combining the two friction models is designed for antagonistic directions in a joint to account for the bidirectional cable drive's backlash and hysteresis characteristics. Owing to this coefficient, the error of the friction model is reduced by approximately 90% during motion direction change. Finally, the accuracy of the torque sensor model is verified experimentally, and the root-mean-square error (RMSE) is about 0.038 N·m (2.8%). The RMSE of extracted interaction torque is about 0.25 N·m (8.1%). This article validates the feasibility of extracting HEI torque via a torque sensor implemented in the upper-limb exoskeleton, which can promote the development of new generations of upper-limb exoskeleton for active rehabilitation or assistance and research on intuitive control of exoskeleton in future.