Abstract
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.
Highlights
Gait is a clear biological feature model of human movement, as each subject has its own biological characteristics, and the deviation from normal gait patterns can be the indication of various diseases
MAIN METHOD In this chapter, we use the gait information location algorithm to process data, and propose a deep neural network composed of convolutional neural network (CNN) and long shortterm memory (LSTM) to classify abnormal gait signals
We proved that the method consisting of taking all image features into consideration to participate in sampling decision-making, ensuring that the feature points with various value distributions are decided according to weight coefficients, extracting more complete feature points based on the SPPnet approach, FIGURE 12
Summary
Gait is a clear biological feature model of human movement, as each subject has its own biological characteristics, and the deviation from normal gait patterns can be the indication of various diseases. Gait analysis can be traced back to the 1960s and is very popular in the context of neurological diseases, such as Parkinson’s disease [1], cerebral palsy [2] and rehabilitation training [3]. This research area does includes the medical field, but it spreads to other fields in [4]. Abnormal or sick gait deviates from the normal pattern in [5]. There can be many different reasons for this deviation, including most commonly different neurological diseases, hemiplegia, or age effects. The impact of various diseases on gait may be different, affecting various gait parameters
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