Human Intention Recognition for Lower Limb Exoskeleton Robot

The rapid and accurate recognition of human lower limb movement intentions is very important for the control system of lower limb exoskeleton robots, this paper innovatively proposes an adaptive inertial weighted improved particle swarm optimization LSTM algorithm (IPSO-LSTM), which not only characterizes the mapping relationship between the Surface electromyography (sEMG) signal and the joint angle of the lower extremity in continuous motion, but also solves the values random setting problems of iterations number, learning rate and hidden layer number. More importantly, the optimization algorithm solves the network over fitting problem and further improves the predict accuracy of the model. Finally, based on the complex system of lower limb rehabilitation exoskeleton robot, the algorithm is applied to the human-exoskeleton cooperative control experiment of active rehabilitation training, the experiment verifies that the IPSO-LSTM algorithm model can meet the requirements of real-time and accuracy of active intention recognition.