Continuous knee joint angle and surface electromyography (SEMG) signal prediction could improve exoskeleton performance. Prediction of SEMG and knee angle is helpful for the physiotherapist for the improvement in remote rehabilitation. Particle Swarm Optimization-Long Short Term Memory (PSO-LSTM) has been used to classify three movements (Flexion, Extension, Ramp Walking) and predict to improve exoskeleton performance. Five healthy subjects participated in testing the effectiveness of the model. Four knee muscles SEMG signals, namely biceps femoris, vastus medialis, rectus femoris and semitendinosus, and knee angle, were used as model inputs. RMSE, r, and R² were taken as evaluation parameters to identify the model's robustness for predicting SEMS signal and knee angle. The proposed model was used to classify three movements (Flexion, Extension, Ramp Walking) with an accuracy of 98.58%. The LSTM-PSO results were compared with random LSTM for predicting and classifying the three movements, and the performance of the proposed model was found to be better. This model could be beneficial in rehabilitating stroke patients in remote areas and designing assistive devices.

连续膝关节角度和表面肌电图 (SEMG) 信号预测可以提高外骨骼性能。SEMG和膝关节角度的预测有助于物理治疗师改善远程康复。粒子群优化-长短期记忆 (PSO-LSTM) 已被用于对三种运动（屈曲、伸展、斜坡行走）进行分类，并预测以提高外骨骼性能。五名健康​​受试者参与测试模型的有效性。四种膝关节肌肉SEMG信号，即股二头肌、股内侧肌、股直肌和半腱肌以及膝关节角度，被用作模型输入。均方根误差、r和R ²被作为评估参数来确定模型预测 SEMS 信号和膝关节角度的鲁棒性。所提出的模型用于对三种运动（屈曲、伸展、斜坡行走）进行分类，准确率为 98.58%。LSTM-PSO 结果与随机 LSTM 对三种运动的预测和分类进行了比较，发现所提出模型的性能更好。该模型可能有益于偏远地区中风患者的康复和辅助设备的设计。