Attention-Based Deep Learning Framework for Hemiplegic Gait Prediction With Smartphone Sensors

Abstract

This research revealed a reliable hemiplegia gait monitoring strategy to help medical practitioners in keeping track of a patient’s status. Although numerous technologies have been utilized in the past to collect motion data from patients, the high costs and huge spaces required make them challenging to use in a home setting for rehabilitation. A telemedicine protocol requires a reliable patient monitoring technique that can automatically record and classify patient movements. To achieve this, we propose an attention-based deep learning framework for hemiplegia gait prediction with smartphone-based sensory data, i.e., accelerometer and gyroscope. Firstly, convolutional neural network long short-term memory (CNN-LSTM) architecture is proposed to automatically learn potential features from the high-frequency sensory data. Moreover, considering the effectiveness of the domain expert knowledge-based hand-engineered features for gait analysis, we combine the automatically learned features and the extracted hand-engineered features from sensory data. Secondly, an attention network is proposed to tune the significance of two different features, considering these two different sourced features may be complementary to each other. Finally, extensive experiments are carried out to establish the effectiveness of the suggested hemiplegia gait prediction method in the evaluation of 5 X 2 fold cross-validation and leave-one-subject-out (LOSO) cross-validation, which is more difficult and practical.