Deep learning analysis based on multi-sensor fusion data for hemiplegia rehabilitation training system for stoke patients

By recognizing the motion of the healthy side, the lower limb exoskeleton robot can provide therapy to the affected side of stroke patients. To improve the accuracy of motion intention recognition based on sensor data, the research based on deep learning was carried out. Eighty healthy subjects performed gait experiments under five different gait environments (flat ground, 10 degrees upslope and downslope, and upstairs and downstairs) by simulating stroke patients. To facilitate the training and classification of the neural network, this paper presents template processing schemes to adapt to different data formats. The novel algorithm model of a hybrid network model based on convolutional neural network (CNN) and Long-short-term memory (LSTM) model is constructed. To mitigate the data-sparse problem, a spatial-temporal-embedded LSTM model (SQLSTM) combining spatial-temporal influence with the LSTM model is proposed. The proposed CNN-SQLSTM model is evaluated on a real trajectory dataset, and the results demonstrate the effectiveness of the proposed model. The proposed method will be used to guide the control strategy design of robot system for active rehabilitation training.

Automated summary

By recognizing the motion of the healthy side, the lower limb exoskeleton robot can provide therapy to the affected side of stroke patients. Deep learning-based research was conducted to improve the accuracy of motion intention recognition. A novel algorithm model and template processing schemes were proposed to adapt to different data formats, and the effectiveness of the proposed model was demonstrated through evaluation on a real trajectory dataset.