Abstract
Human gait phase recognition is a significant technology for rehabilitation training robot, human disease diagnosis, artificial prosthesis, and so on. The efficient design of the recognition method for gait information is the key issue in the current gait phase division and eigenvalues extraction research. In this paper, a novel voting-weighted integrated neural network (VWI-DNN) is proposed to detect different gait phases from multidimensional acceleration signals. More specifically, it first employs a gait information acquisition system to collect different IMU sensors data fixed on the human lower limb. Then, with dimensionality reduction and four-phase division preprocessing, key features are selected and merged as unified vectors to learn common and domain knowledge in time domain. Next, multiple refined DNNs are transferred to design a multistream integrated neural network, which utilizes the mixture-granularity information to exploit high-dimensional feature representative. Finally, a voting-weighted function is developed to fuse different submodels as a unified representation for distinguishing small discrepancy among different gait phases. The end-to-end implementation of the VWI-DNN model is fine-tuned by the loss optimization of gradient back-propagation. Experimental results demonstrate the outperforming performance of the proposed method with higher classification accuracy compared with the other methods, of which classification accuracy and macro-F1 is up to 99.5%. More discussions are provided to indicate the potential applications in combination with other works.
Highlights
As the most common form of human behavior, walking style is related to health status and individual di erences, which can be shown by the di erences of the gait phase [1]
We describe a system that uses three inertial sensor modules to obtain the acceleration information of the lower limbs of the human body. e collected acceleration data was reduced by the Principal Component Analysis (PCA) algorithm, which focuses on extracting the feature information of the original data and searches for a set of orthogonal low-wiki functions to represent a set of highdimensional data, improving the recognition rate and recognition speed [20, 21]. en, the paper divides the human gait into three phases and proposes a method of dividing the three gait phases
F1 combines the results of P and R, and when F1 is high, it indicates that Precision and Recall are both high, and this evaluation index is relatively effective
Summary
As the most common form of human behavior, walking style is related to health status and individual di erences, which can be shown by the di erences of the gait phase [1]. Detecting results of the gait phase can provide references for disease diagnosis and rehabilitation [2, 3], which is of great signi cance to the patients’ clinical rehabilitation. Yan et al [4] proposed that gait phase detection can be used to facilitate the development human auxiliary equipment, such as the medical ankle joint (AF), hip joint (HK), and knee ankle joint (KAF) orthopedic devices, as well as exoskeletons and other equipment. Gait phase detection plays an important role in sports medicine [7] and rehabilitation medicine [8]
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