A Study on Lower Limb Movement Intention Recognition Based on Multi-Source Information Fusion

In order to enhance the suppleness of a lower limb rehabilitation medical robot during the re-habilitation process, this study proposes a multi-source information fusion lower limb motion intention recognition method based on surface electromyographic signals (sEMG) and lower limb joint angles. To solve the problem of data traffic surge during the collection process, a multi-source current limiting sliding time window algorithm (MLS) is proposed. The MLS algorithm controls the data flow through a flow limiting and sliding time window mechanism to ensure the efficiency and stability of the system in handling large data volumes. On this basis, the study combines the Back Propagation Generalized Algorithm Neural-network (BPGN) to construct a prediction model for lower limb joint angles. The experimental results show that under the same conditions of the algorithm, the fusion of multi-source information reduces the average error of knee joint angle prediction by 10.8° and the average error of ankle joint angle prediction by 7.2° compared with the method using a single lower limb joint angle signal. Under the same condition of input signal, the multivariate flow-limiting sliding time-window BPGN reduced the average knee joint error by 13.6° and the average ankle joint angle error by 8.5° compared to the BPGN intent recognition. The multivariate flow-limited sliding time window BPGN reduced the mean knee error by 11.2° and the mean ankle angle error by 7.4° compared to Radial Basis Function (RBF) Neural-network intent recognition. By integrating the sEMG signal and lower limb joint angle information, the system can more accurately capture the patient’s movement intention and realize more precise lower limb rehabilitation training.