Abstract
Hemiparesis is one of the common sequelae of neurological diseases such as strokes, which can significantly change the gait behavior of patients and restrict their activities in daily life. The results of gait characteristic analysis can provide a reference for disease diagnosis and rehabilitation; however, gait correlation as a gait characteristic is less utilized currently. In this study, a new non-contact electrostatic field sensing method was used to obtain the electrostatic gait signals of hemiplegic patients and healthy control subjects, and an improved Detrended Cross-Correlation Analysis cross-correlation coefficient method was proposed to analyze the obtained electrostatic gait signals. The results show that the improved method can better obtain the dynamic changes of the scaling index under the multi-scale structure, which makes up for the shortcomings of the traditional Detrended Cross-Correlation Analysis cross-correlation coefficient method when calculating the electrostatic gait signal of the same kind of subjects, such as random and incomplete similarity in the trend of the scaling index spectrum change. At the same time, it can effectively quantify the correlation of electrostatic gait signals in subjects. The proposed method has the potential to be a powerful tool for extracting the gait correlation features and identifying the electrostatic gait of hemiplegic patients.
Highlights
With the aging of the global population, strokes and other neurological diseases are occurring more frequently
The rest of this paper is arranged as follows: The second part will introduce the principle of electrostatic sensing method and the experimental process of electrostatic gait signal acquisition, as well as how to obtain electrostatic gait signals of hemiplegic patients and healthy control subjects; the third part will introduce in detail the improvement of the traditional DCCA cross-correlation coefficient method; and the fourth part uses the traditional method to analyze the electrostatic gait signals of subjects, from which we find that the traditional method has some shortcomings in the analysis of Sensors 2019, 19, x FOR PEER REVIEW
The results showed that the improved method improved the stability and accuracy of the data results compared with the traditional method, and helped to better identify the electrostatic gait signal of hemiparesis
Summary
With the aging of the global population, strokes and other neurological diseases are occurring more frequently. There are two main methods of gait measurement; one is the contact measurement method, which is usually obtained through the subject wearing an inertial unit sensor [5,6] or photoelectric sensor [7,8]; the other is the non-contact method, in which a video analysis system is used [9]. In Chen’s work [12], the equivalent capacitance model of the Sensors 2019, 19, 2529; doi:10.3390/s19112529 www.mdpi.com/journal/sensors
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