Gait Parameter Identification of Lower Limb Exoskeleton based on Neural Network

With the continuous development and improvement of related technologies, as well as the increasing aging of the population, the increasing demand for assisted walking equipment for the elderly and other issues, the lower limb exoskeleton will play a more important role in the field of rehabilitation medicine. The physical condition and rehabilitation needs of each patient are unique, and the lower limb exoskeleton based on neural network can automatically adjust the rehabilitation plan according to the specific situation of the patient to achieve personalized treatment. This personalized program can better meet the needs of patients and improve the rehabilitation effect. With the continuous development of artificial intelligence and robotics, lower limb exoskeleton based on neural network is expected to become an important direction of rehabilitation technology in the future, and its research results will bring revolutionary changes to the rehabilitation medical field. Therefore, based on the relevant theoretical knowledge of neural networks, this paper will explore and discuss several different neural network related algorithms, namely GNN, recurrent neural network, long short-term memory network (LSTM) combined with Conformer framework, and explore their specific effects on the parameter identification of lower limb exoskeleton and gait recognition.