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.

Gait symmetry is a clinically relevant indicator that distinguishes between normal gait and pathological gait, and it can provide a strong basis for mobility assessment and rehabilitation interventions. This paper proposes a comprehensive quantitative assessment method for gait symmetry based on human electrostatic gait signals (EGSs). This study is the first application of EGSs to examine gait symmetry in terms of trend symmetry, amplitude symmetry, and time symmetry in 30 subjects, including healthy subjects (HSs), hemiplegic patients (HPs), and Parkinson’s disease patients (PDs). The trend symmetry index ( SI _trend )compares the two sides of the continuous waveform of the gait waveforms using the eigenvector; the amplitude symmetry indexes( SI_{A_IC}, SI_{A_SE} )compare the amplitude of EGSs at the moment of initial contact (IC) with the ground and separation (SE) from the ground; the time symmetry indexes( SI_TSP, SI_TSW ) compare the durations of the support phase and the swing phase. The results showed that the HPs and PDs showed significant asymmetry in gait trends, gait signal amplitude, and gait temporal parameters on both sides compared to the HSs. In addition, the HPs showed greater asymmetry than the PDs. The Kruskal–Wallis test was used to verify that the distributions of the 5 symmetry indexes differed significantly among the three different groups (p<0.05); trend symmetry was the most effective feature for distinguishing among the three groups. This paper provides a comprehensive and effective EGSs-based analysis of gait symmetry in terms of continuous gait waveforms and discrete gait parameters.

The parameters of gait phases obtained by detecting gait events can provide a robust basis for the pathological diagnosis and rehabilitation of patients with lower limb disorders. In this paper, a method is proposed to divide the inner-stance phase and inner-swing phase by detecting the gait events of heel-off (HO) and swing to the highest position (SHP) in the stepping electrostatic gait signal (EGS) and to estimate the subphase time parameters. The gait events are detected by using the local extreme points of the integrated signal of the EGS. Four subphase temporal parameters are obtained after combining the local extrema of the EGS representing the initial contact (IC) and toe-off (TO) for the division of the inner-stance and inner-swing phases. Compared with the motion capture system (VICON), the detection accuracy of the proposed method reaches 98.5%, and the measurement error is within the range of ±60 ms. Pearson’s coefficient ‘r’ was used to verify the correlation between the four gait subphases estimated by the two methods, and excellent agreement was obtained (r > 0.95, p < 0.05). Most of the measurement results of the four subphase temporal gait parameters are within the 95% confidence interval of the Bland–Altman analysis chart. The repeatability of the proposed method was calculated through intraclass correlation coefficients (ICC) and showed good test-retest reliability (ICC > 0.85, p < 0.01). This method detects gait events and estimates subphase duration with high accuracy and can provide a foundation for the gait analysis of EGSs.

Falls are one of the most serious health risks faced by older adults worldwide, and they can have a significant impact on their physical and mental well-being as well as their quality of life. Detecting falls promptly and accurately and providing assistance can effectively reduce the harm caused by falls to older adults. This paper proposed a noncontact fall detection method based on the human electrostatic field and a VMD-ECANet framework. An electrostatic measurement system was used to measure the electrostatic signals of four types of falling postures and five types of daily actions. The signals were randomly divided in proportion and by individuals to construct a training set and test set. A fall detection model based on the VMD-ECA network was proposed that decomposes electrostatic signals into modal component signals using the variational mode decomposition (VMD) technique. These signals were then fed into a multichannel convolutional neural network for feature extraction. Information fusion was achieved through the efficient channel attention network (ECANet) module. Finally, the extracted features were input into a classifier to obtain the output results. The constructed model achieved an accuracy of 96.44%. The proposed fall detection solution has several advantages, including being noncontact, cost-effective, and privacy friendly. It is suitable for detecting indoor falls by older individuals living alone and helps to reduce the harm caused by falls.

Accurate assessment of mobility is of great significance for disease diagnosis and rehabilitation guidance. In response to the needs of hemiplegic patients for accurate quantitative assessment of mobility during disease diagnosis and rehabilitation, this paper proposes a gait analysis method based on the fusion of bimodal gait signals from human electrostatic field and Kinect, which can objectively and quantitatively assess the gait abnormalities of hemiplegic patients. Kinematic data and human electrostatic gait signals were recorded simultaneously during the walking process of the subjects, and 10 quantitative indexes of motion ability and symmetry of hip, knee, and ankle joint, muscle force control ability, gait symmetry, gait balance, and gait stability were extracted. Gait index of hemiplegic patients was quantitatively assessed by an improved principal component analysis method, which was used to quantitatively assess the lower limb motor function of hemiplegic patients. The results showed that the hemiplegic gait index was effective in differentiating hemiplegic patients with different Brunnstrom stages, and showed a significant negative correlation with the Fugl-Meyer lower extremity motor function scale scores (P<0.05), with an absolute value of the correlation coefficient as high as 0.96. This paper suggests that the hemiplegic gait index is a reliable mobility assessment tool to better support traditional clinical decision-making and improve the efficiency of rehabilitation therapy.