Accurate estimation of gait parameters is essential for obtaining quantitative information on motor deficits in Parkinson’s disease and other neurodegenerative diseases, which helps determine disease progression and therapeutic interventions. Due to the demand for high accuracy, unobtrusive measurement methods such as optical motion capture systems, foot pressure plates, and other systems have been commonly used in clinical environments. However, the high cost of existing lab-based methods greatly hinders their wider usage, especially in developing countries. In this study, we present a low-cost, noncontact, and an accurate temporal gait parameters estimation method by sensing and analyzing the electrostatic field generated from human foot stepping. The proposed method achieved an average 97% accuracy on gait phase detection and was further validated by comparison to the foot pressure system in 10 healthy subjects. Two results were compared using the Pearson coefficient r and obtained an excellent consistency (r = 0.99, p < 0.05). The repeatability of the purposed method was calculated between days by intraclass correlation coefficients (ICC), and showed good test-retest reliability (ICC = 0.87, p < 0.01). The proposed method could be an affordable and accurate tool to measure temporal gait parameters in hospital laboratories and in patients’ home environments.

In this paper, we mainly study the emotion recognition algorithm based on ECG signals, extract the correlation feature and time-frequency domain statistical feature of ECG signals, and introduce SVW, CART and KNN three classification algorithms commonly used in emotion recognition. By comparing the accuracy of emotion recognition in the application of three classification algorithms between the correlation features of ECG signals and traditional time-frequency domain features, we found that the use of correlation features of ECG signals can get a higher recognition rate, which is 16.7%∼19.7% higher than that of the traditional feature. In addition, among the three classification algorithms, KNN algorithm can get the highest emotion recognition accuracy. In order to further improve the accuracy of emotion recognition, Max-Min Ant System is combined with KNN classification algorithm in this paper to optimize the feature combination. The overall recognition rate reaches 92%, which is 16.9% higher than the accuracy of emotion recognition directly using KNN classification algorithm.

In this paper, we propose a new fingerprint sensing technology based on electrostatic imaging, which can greatly improve fingerprint sensing distance. This can solve the problem of the existing capacitive fingerprint identification device being easy to damage due to limited detection distance and a protective coating that is too thin. The fingerprint recognition sensor can also be placed under a glass screen to meet the needs of the full screen design of the mobile phone. In this paper, the electric field distribution around the fingerprint is analyzed. The electrostatic imaging sensor design is carried out based on the electrostatic detection principle and MEMS (micro-electro-mechanical system) technology. The MEMS electrostatic imaging array, analog, and digital signal processing circuit structure are designed. Simulation and testing are carried out as well. According to the simulation and prototype test device test results, it is confirmed that our proposed electrostatic imaging-based fingerprint sensing technology can increase fingerprint recognition distance by 46% compared to the existing capacitive fingerprint sensing technology. A distance of more than 439 μm is reached.

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 paper 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.

Gait stability is an important indicator of human health and physical ability, and it is of great significance for early detection, diagnosis, and rehabilitation of diseases. This article proposes the first comprehensive and in-depth method for quantitatively evaluating gait stability using electrostatic gait signals. A quantitative evaluation of gait stability was conducted on the electrostatic gait signals (EGSs) of ten healthy subjects (HSs) and ten hemiplegic patients (HPs) from three perspectives: variability of gait phase temporal parameters, nonlinear local dynamic stability, and energy frequency band distribution differences. The coefficient of variation ( CV ( x ) ) reflects the degree of variation of each temporal parameter. Local dynamic stability is characterized by using the short-term largest Lyapunov exponent (λ * _S ) to reflect the sensitivity to local perturbations and to characterize gait stability. Power spectral entropy ( PSE ) is used to quantify the complexity and instability of the signal in the frequency domain to characterize gait stability. The results showed that the CV ( x ), λ * _S , and PSE were all significantly greater in HPs than in HSs. The Mann–Whitney test was used to perform significance testing on each indicator between the HS and HP groups. The results showed that, except for the variability of the affected side support phase ( CV ( T_LHI )), the variability of the other 13 gait temporal parameters, short-term largest Lyapunov exponent (λ * _S ), and PSE differed significantly between the groups (p<0.05). This research explores a feasible technical approach to using electrostatic gait signals for gait analysis and quantitatively assessing the gait stability of subjects.

Regulating the parameters of the atmospheric pressure plasma jet (APPJ) is meaningful for industrial applications. Since plasma is a typical functional fluid in the magnetic field, it is possible to control the discharge characteristics via the Lorentz force. In this study, the effects of a locally divergent magnetic field on the generation and propagation of APPJ were examined experimentally. The experiments used a coplanar dielectric barrier discharge (CDBD) device driven by a 30 kHz AC high-voltage source to generate a helium APPJ. A locally divergent magnetic field of 250 mT (maximum) was applied coupled with the electric field, and noticeable enhancement was observed. The results showed that the magnetic field changed the motion state of electrons and promoted collision ionization, leading to a 40% improvement in the APPJ length (0.6 cm) and a 23% increase in the intensity of line O (777.2 nm). In addition, the spatiotemporal evolution and flow field of APPJ were diagnosed by ICCD and schlieren technique. The combination of electric and magnetic fields may effectively optimize the APPJ in practical applications.