Modeling and Evaluating Full-Cycle Natural Gait Detection Based on Human Electrostatic Field

Gait analysis is a technique facilitating disease diagnosis, rehabilitation, and mobility assessment. The gait detection approach based on the human electric field is noncontact and portable while providing real-time gait data. This article presents a noncontact gait detection method based on a full-cycle gait characteristic detection model. Based on electrodynamics theory, a complex variable function method evaluating static fields under complex boundary conditions was used, an equivalent plantar capacitance calculation model was proposed, and a full-cycle gait characteristic detection model was established. The simulation results showed that the calculation model greatly improved the plantar capacitance accuracy, and the rationality and validity of the model were qualitatively verified through human electrostatic potential and gait data, so the detection model reflected the time-domain signal and detailed characteristics of the full-cycle gait. VICON was used to acquire the simulated abnormal gait and to verify the correctness of the detection model and system. The clinical gaits of patients with Parkinson's and hemiplegia were collected to verify the effectiveness of the method to reflect full-cycle time-domain signals and extract abnormal gait information. This study provides a theoretical basis and feasible gait analysis method to obtain full-cycle information about natural gait.