Cyclostationary Modeling of Surface Electromyography Signal During Gait Cycles and Its Application for Cerebral Palsy Diagnosis

Cerebral palsy(CP) is a group of permanent movement disorders that appear in early childhood.The electromyography(EMG) signal analysis and the gait analysis are two most commonly used methods in the clinic. In this paper, a cyclostationary model of the EMG signal is proposed. The model can combine the aforementioned two methods. The EMG signal acquired during the gait cycles is assumed to be cyclostationary due to the physiological characteristics of the EMG signal production. Then, the spectral correlation density is used to analyze the cyclic frequency(corresponding to the gait cycles) and spectral frequency(the frequency of EMG signal) in a waterfall representation of the two kinds of frequencies. The experiments show that the asymptomatic(normal) subjects and symptomatic subjects(with CP) can be distinguished from the spectral correlation density in a range of cyclic frequencies.

Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

Gait is a biological typical that defines the method by that people walk.Walking is the most significant performance which keeps our day-to-day life and physical condition.Surface electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent.Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment.Several approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)approaches.This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG signals.The EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is fused.In addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait recognition.At last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR technique.The results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.

An experimental investigation into electromyography, constitutive relationship and morphology of crucian carp for biomechanical “digital fish”

Currently, the integrated biomechanical studies on fish locomotion come into focus, so it is urgent to provide reliable and sys- tematic experimental results, and to establish a biomechanical ＂digital fish＂ database for some typical fish species. Accord- ingly, based on the control framework of ＂Neural Control - Active Contraction of Muscle - Passive Deformation＂, the elec- tromyography （EMG） signals, the mechanical properties and the constitutive relationship of skin, muscle, and body trunk, as well as morphological parameters of crucian carp, are investigated with experiments, from which a simplified database of bio- mechanical ＂digital fish＂ is established. First, the EMG signals from three lateral superficial red muscles of crucian carp, which was evolving in the C-start movement, were acquired with a self-designing amplifier. The modes of muscle activity were also investigated. Secondly, the Young＇s modulus and the reduced relaxation function of crucian carp＇s skin and muscle were de- termined by failure tests and relaxation tests in uniaxial tensile ways, respectively. Viscoelastic models were adopted to deduce the constitutive relationship. The mechanical properties and the angular stiffness of different sites on the crucian carp＇s body trunk were obtained with dynamic bending experiments, where a self-designing dynamic bending test machine was employed. The conclusion was drawn regarding the body trunk of crucian carp under dynamic bending deformation as an approximate elastomer. According to the above experimental results, a possible benefit of body effective stiffness increasing with a little energy dissipation was discussed. Thirdly, the distribution of geometric parameters and weight parameters for a single experi- mental individual and multiple individuals of crucian carp was studied with experiments. Finally, considering all the above re- suits, generic experimental data were obtained by normalization, and a preliminary biomechanical ＂digital fish＂ database for crucian carp was established.

Stretchable human-machine interface based on skin-conformal sEMG electrodes with self-similar geometry

Current stretchable surface electrodes have attracted increasing attention owing to their potential applications in biological signal monitoring, wearable human-machine interfaces（HMIs） and the Internet of Things. The paper proposed a stretchable HMI based on a surface electromyography（sEMG） electrode with a self-similar serpentine configuration. The sEMG electrode was transfer-printed onto the skin surface conformally to monitor biological signals, followed by signal classification and controlling of a mobile robot. Such electrodes can bear rather large deformation（such as 〉30%） under an appropriate areal coverage. The sEMG electrodes have been used to record electrophysiological signals from different parts of the body with sharp curvature, such as the index finger,back of the neck and face, and they exhibit great potential for HMI in the fields of robotics and healthcare. The electrodes placed onto the two wrists would generate two different signals with the fist clenched and loosened. It is classified to four kinds of signals with a combination of the gestures from the two wrists, that is, four control modes. Experiments demonstrated that the electrodes were successfully used as an HMI to control the motion of a mobile robot remotely.