Design and Verification of an Adaptive Force Feedback System for Upper Limb Exoskeletons Based on Multimodal Sensors

To enhance the accuracy and naturalness of human-machine interaction (HMI) for upper limb exoskeletons, the key lies in overcoming the adaptive bottleneck of their force feedback systems. Traditional methods have limitations in multimodal information fusion and dynamic control. Therefore, this study aims to develop an adaptive force feedback system based on multimodal sensors. By deeply fusing surface electromyography (sEMG) and mechanical sensor data, and introducing deep learning algorithms, a control strategy capable of dynamically adapting to user intentions was constructed. Notably, we independently designed and fabricated core temperature and pressure sensors. Experimental data shows that their characteristic equations are y = 0.041x - 0.90 and y = 0.1x, respectively, and their performance is highly consistent with that of standard sensors. This study confirms that the proposed scheme can effectively improve the sensing accuracy and adaptive capability of the force feedback system, providing a reliable hardware foundation and a novel technical approach for achieving more intelligent and collaborative human-machine interaction.