Zhonghua Liu

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.

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 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.