Abstract
Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation—while ignoring the inherent correlation in high-dimensional spaces—which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.
Highlights
In recent years, robotic exoskeleton has become an emerging technology in medical, living, industrial and military applications
These two indicators are used to evaluate the quality of the model results. precision is used to measure the accuracy of the retrieval system. recall is used to measure the recall of the retrieval system
In order to verify the effectiveness of the proposed recognition model, we implemented two other gait-phase-recognition methods, namely long short-term memory (LSTM) and LSTM + convolutional neural networks (CNN)
Summary
Robotic exoskeleton has become an emerging technology in medical, living, industrial and military applications. Lower extremity exoskeleton has important research value in the medical field, its main potential is to enhance the patient’s ability to move in rehabilitation therapy, and enhance physical function after receiving treatment, and hope to improve their quality of life as much as possible. Gait recognition technology is an important technical guarantee for the robot to process a large amount of instantaneous time series data, which is one of the most important features to display the posture and phase of each specific patient [1]. In medical disease-diagnosis and rehabilitation research, effective analysis of gait phases has achieved remarkable results, which has been used in clinical treatment plans for stroke, Parkinson’s disease, brain trauma and other diseases [3,4]. Mathieu et al [6] proposed a novel adaptive dynamic time
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