This page has only limited features, please log in for full access.
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.
Shanshan Tian; Mengxuan Li; Yifei Wang; Xi Chen. Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients. Sensors 2019, 19, 2529 .
AMA StyleShanshan Tian, Mengxuan Li, Yifei Wang, Xi Chen. Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients. Sensors. 2019; 19 (11):2529.
Chicago/Turabian StyleShanshan Tian; Mengxuan Li; Yifei Wang; Xi Chen. 2019. "Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients." Sensors 19, no. 11: 2529.
Non-contact human-computer interactions (HCI) based on hand gestures have been widely investigated. Here, we present a novel method to locate the real-time position of the hand using the electrostatics of the human body. This method has many advantages, including a delay of less than one millisecond, low cost, and does not require a camera or wearable devices. A formula is first created to sense array signals with five spherical electrodes. Next, a solving algorithm for the real-time measured hand position is introduced and solving equations for three-dimensional coordinates of hand position are obtained. A non-contact real-time hand position sensing system was established to perform verification experiments, and the principle error of the algorithm and the systematic noise were also analyzed. The results show that this novel technology can determine the dynamic parameters of hand movements with good robustness to meet the requirements of complicated HCI.
Kai Tang; Pengfei Li; Chuang Wang; Yifei Wang; Xi Chen. Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics. Sensors 2018, 18, 1677 .
AMA StyleKai Tang, Pengfei Li, Chuang Wang, Yifei Wang, Xi Chen. Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics. Sensors. 2018; 18 (6):1677.
Chicago/Turabian StyleKai Tang; Pengfei Li; Chuang Wang; Yifei Wang; Xi Chen. 2018. "Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics." Sensors 18, no. 6: 1677.