Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10−3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10−2. The R2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.
The body weight support rehabilitation training system has now become an important treatment method for the rehabilitation of lower limb motor dysfunction. In this paper, a pelvic brace body weight support rehabilitation system is proposed, which follows the center of mass height (CoMH) of the human body. It aims to address the problems that the existing pelvic brace body weight support rehabilitation system with constant impedance provides a fixed motion trajectory for the pelvic mechanism during the rehabilitation training and that the patients have low participation in rehabilitation training. The system collectes human lower limb motion information through inertial measurement unit and predicts CoMH through artificial neural network to realize the tracking control of pelvic brace height. The proposed CoMH model was tested through rehabilitation training of hemiplegic patients. The results showed that the range of motion of the hip and knee joints on the affected side of the patient was improved by 25.0% and 31.4%, respectively, and the ratio of swing phase to support phase on the affected side was closer to that of the gait phase on the healthy side, as opposed to the traditional body weight support rehabilitation training model with fixed motion trajectory of pelvic brace. The motion trajectory of the pelvic brace in CoMH mode depends on the current state of the trainer so as to realize the walking training guided by active movement on the healthy side of hemiplegia patients. The strategy of dynamically adjustment of body weight support is more helpful to improve the efficiency of walking rehabilitation training.