Gait Phase Classification of Lower Limb Exoskeleton Based on a Compound Network Model

Abstract

The classification of lower limb gait phase is very important for the control of exoskeleton robots. In order to enable the exoskeleton to determine gait phase and provide appropriate assistance to the wearer, we propose a compound network based on CNN-BiLSTM. The method uses data from inertial measurement units placed on the leg and pressure sensor arrays placed on the sole as inputs to the model. The convolutional neural network (CNN) is used to obtain the local key features of gait data, and then the bidirectional long short-term memory (BiLSTM) network is used to extract the serialized gait phase information from the local key features to obtain the high-level feature expression. Finally, the seven phases of both feet were obtained through the classification of the softmax layer. We designed a gait acquisition system and collected the gait data from seven subjects at varying walking speeds. In the test set, the highest gait phase classification accuracy can reach 95.09%. We compared the proposed model with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. The experimental results show that the average accuracy of CNN-BiLSTM network from seven subjects is 0.417% higher than that of the LSTM network and 0.596% higher than that of the GRU network. Therefore, the ability of the CNN-BiLSTM network to classify gait phases can be applied in designing exoskeleton controllers that can better assist for different gait phases correctly to assist the wearer to walk.

Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review

“…After calculating the mean absolute error and processing a Bland-Altman analysis, they showed that the differences between both systems are within a mean ± 1.96 standard deviations. Another study [ 63 ] used a passive lower limb weight-bearing exoskeleton equipped with IMUs at the thigh and shank of the exoskeleton. For processing these data, investigators used a CNN-BiLSTM Network Model.…”

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting

“…The model can accurately identify the four stages in the gait cycle, and the accuracy and F-value are better than other algorithms. Xia et al [ 26 ] used a model based on CNN-BiLSTM to classify seven gait phases of both legs through IMU data of lower limb and plantar pressure data, with a maximum accuracy of 95.09%. Chen et al [ 27 ] proposed a lower-limb exoskeleton gait pattern-recognition method based on LSTM and CNN, which can recognize five common gait patterns with an average recognition accuracy of 97.78%.…”