Abstract
In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.
Highlights
The movement intention of the human body is generated from the brain and transmitted to the muscle cells through the nerves
An effective method to identify human movement patterns and movement intentions is by collecting human electromyography (EMG) signals to control the movement of an exoskeleton in real time [4,5]
We know that when the human body wears an exoskeleton, the accurate recognition of the human movement mode guarantees the switching to the accurate movement mode during the mixed movement control of the exoskeleton
Summary
The movement intention of the human body is generated from the brain and transmitted to the muscle cells through the nerves. The form and amplitude of the electrical signal of the muscle directly reflect the movement pattern of the human body [1,2,3]. In order to enable wearable devices such as prosthetics and exoskeletons to switch smoothly between multiple movement modes, many. An effective method to identify human movement patterns and movement intentions is by collecting human electromyography (EMG) signals to control the movement of an exoskeleton in real time [4,5]. The lower-limb prosthetic system is expected to seamlessly switch among motion modes, and the overall recognition rate can be increased to 86%. Joshi et al [8] used the Bayesian information criterion (BIC) and some standard feature extraction methods and linear discriminant analysis (LDA)
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