Spring damping based control for a novel lower limb rehabilitation robot with active flexible training planning

Abstract

BACKGROUND: During neurological rehabilitation training for patients with lower limb dysfunction, active rehabilitation training based on interactive force recognition can effectively improve participation and efficiency in rehabilitation training. OBJECTIVE: This study proposes an active training strategy for lower-limb rehabilitation robots based on a spring damping model. METHODS: The active training strategy included a kinetic model of the human-machine system, calculated and verified using a pull-pressure force sensor We used a dynamic model of the human-machine system and tensile force sensors to identify the human-machine interaction forces exerted by the patient Finally, the spring damping model is used to convert the active interaction force into the offset angle of each joint, obtaining the active interaction force followed by the active movement of the lower limbs RESULTS: The experimental results showed that the rehabilitation robot could follow the active interaction force of the subject to provide assistance, thus generating the following movement and effectively helping patients improve joint mobility. CONCLUSION: The active flexibility training control strategy based on the virtual spring damping model proposed in this study is feasible, and motion is stable for patients with lower limb dysfunction after stroke Finally, the proposed active training method can be implemented in future work in other rehabilitation equipment and combined virtual reality technology to improve rehabilitation training experience and increase patient participation.