Adaptive Impedance Control of Lower Limb Rehabilitation Robot Optimized by the Sparrow Search Algorithm for Sports Fracture Rehabilitation

Traditional lower limb rehabilitation robots suffer from low motion intention recognition accuracy and poor human-robot compliance, which may cause secondary injury to athletes with lower limb fractures. To solve this problem, this study proposes a hybrid control system combining the sparrow search algorithm optimized back propagation neural network (SSA-BP) and adaptive impedance control (AIC). The SSA-BP model is adopted to extract features from surface electromyography (sEMG) signals and recognize human motion intention, while the AIC strategy dynamically adjusts robot stiffness and damping to realize compliant human-robot interaction. Validated on a public lower limb multimodal dataset, the proposed system reduces mean squared error by 61.95% compared with conventional BP methods. Meanwhile, the R 2 is 0.952, which is superior to all methods. Under muscle fatigue and electrode displacement interference, the joint angle tracking root mean square error is only 1.52 ∘ , and the steady-state recovery time is 22.4 ms. The robot maintains low interaction torque and smooth mechanical output during the whole rehabilitation process. Under the three conditions of high-frequency noise, random phase shift, and sudden load interference, the SSA-BP-AIC model also maintains high performance. This proposed control framework exhibits high accuracy, anti-disturbance ability and real-time performance. It can ensure training safety and comfort for fractured athletes, and provides a reliable mechanical control solution for sports rehabilitation engineering.