Abnormal Gait Recognition Algorithm Based on LSTM-CNN Fusion Network

This paper presents a novel approach to human gait analysis with a sensor-based technique involving a wearable inertial measurement unit (IMU). The proposed system emphasizes the detection of certain abnormal gait patterns, including hemiplegic, tiptoe, and cross-threshold gait. First, we use the dynamic step conjugate gradient algorithm to calculate the attitude of the gait data, and we then use the gait feature information location algorithm to segment the attitude data. The segmented attitude data are used as input in the classification model. In this paper, we propose an algorithm based on a long short-term memory network and convolutional neural network (LCWSnet) for diagnosis and classification of abnormal gait patterns using the leg Euler angle information, and parameters related to features can be adjusted adaptively according to the feedback of objectives and optimization functions. We optimize the convergence layer of the LSTM-CNN model and improve the classification accuracy of abnormal gait. The experimental results demonstrate that the proposed LCWSnet-based technique is able to detect gait abnormality in the data. The proposed personalized gait classification approach is accurate and reliable and can be implemented for the abnormal gait.