How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code.

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89 s for all subjects, and the execution time of a single movement was 381 ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.

A Real-Time Bionic Method Inspired by Neural Oscillators for Estimation and Extraction of Pathological Tremor.

The typical representative of pathological tremor is Parkinson's disease. One of the pathogenesis is that the synchronized neural oscillations within and between brain areas are affected. Inspired by this, this work proposes an algorithm based on neural oscillator to extract voluntary motion and estimate tremor motion in real time, which is named as RTBNO. This algorithm is composed of multiple adaptive modified Hopf oscillators linear combiner. The combiner is divided into two parts: one is used to estimate tremor motion and the other is applied to estimate voluntary motion. As it is updated iteratively in real time, this method has no phase delay. The performance of the proposed method was verified by the simulated action tremor and the actual experimental results of twenty Parkinson's disease patients. For the rest tremor signals of patients, the mean Root Mean Square Error (RMSE) values between the estimated signal and the actual signal was 0.0272±0.0077. The mean RMSE values between the estimated voluntary movement from action tremor and the actual voluntary movement were 0.0360±0.0097 (pick and put motion) and 0.0380±0.0083 (drawing motion). The execution time for the corresponding 10 seconds data was 0.0478s. The comparison results between the proposed method and the existing methods demonstrated the effectiveness of the proposed method.

EWT-IIT: a surface electromyography denoising method.

Surface electromyography (sEMG) is often interfered by noise, which has a very important impact on the follow-up research based on sEMG signals, such as motion intention recognition, disease diagnosis, and human-computer interaction. In this paper, an sEMG denoising algorithm based on empirical wavelet transform (EWT) and improved interval thresholding (IIT) is proposed to eliminate noise interference of sEMG signals. The proposed method uses EWT to decompose the original sEMG with noise into several empirical intrinsic modal functions (EIMFs) and then applies the IIT function proposed in this paper to conduct threshold processing for each EIMF; this method is called EWT-IIT. Ten healthy subjects participated in the experiment; the corresponding sEMG signals were analyzed. The signal-to-noise ratio (SNR), root mean square error (RMSE), and [Formula: see text] were used to evaluate the effect of denoising. The simulated and experimental results show that the IIT function proposed in this paper combines the advantages of hard threshold function and soft threshold function, and EWT-IIT method can effectively remove the noise with the best denoising effect.

Arch index measurement method based on plantar distributed force.

In this paper, the solution method for foot arch index (FAI) based on plantar force measurement was proposed. The entire pelma (EP) was divided into three partitions: posterior heel (HP), lateral side of the sole (SL) and medial side of the sole (SM) according to the three-point support mechanics mechanism of the foot and ankle. A distributed force platform was established to obtain the mean positions of the center of pressure (CoP) trajectories on SL, SM, HP, and EP, which were defined as A, B, C, and O, respectively. Based on the principle that the arch height influences the distance from point O to the boundary of triangle ABC, the area ratio of triangle BOC to triangle ABC was defined as FAI. Arch height index (AHI) measurement of thirty participants by combined calipers was compared with FAI measurement of their right feet. The arches were classified based on AHI, and ANOVA was performed. The Pearson correlation coefficient between the FAI method and the AHI method is 0.79 (p＜0.0001). The Bland-Altman analysis showed good agreement. ANOVA indicated FAI was statistically significant (F = 18.81,p＜0.001), and there were statistical differences between groups. These results suggest that the proposed distributed force measurement method can provide support surface boundary (triangle ABC) information related to point O.

Real-time modeling and feature extraction method of surface electromyography signal for hand movement classification based on oscillatory theory.

Objective.Research of surface electromyography (sEMG) signal modeling and feature extraction is important in human motion intention recognition, prosthesis and exoskeleton robots. However, the existing methods mostly use the signal segmentation processing method rather than the point-to-point signal processing method, and lack physiological mechanism support.Approach. In this study, a real-time sEMG signal modeling and separation method is developed based on oscillatory theory. On this basis, an sEMG signal feature extraction method is constructed, and an ensemble learning method is combined to achieve real-time human hand motion intention recognition.Main results.The experimental results show that the average root mean square difference value of the sEMG signal modeling is 0.3838 ± 0.0591, and the average accuracy of human hand motion intention recognition is 96.03 ± 1.74%. On a computer with Intel (R) Core (TM) i5-8250U CPU running Matlab 2016Rb, the execution time for the sEMG signal with an actual duration of 2 s is 0.66 s.Significance. Compared with several existing methods, the proposed method has better modeling accuracy, motion intention recognition accuracy and real-time performance. The method developed in this study may provide a new perspective on sEMG modeling and feature extraction for hand movement classification.

Review of the Upright Balance Assessment Based on the Force Plate.

Quantitative assessment is crucial for the evaluation of human postural balance. The force plate system is the key quantitative balance assessment method. The purpose of this study is to review the important concepts in balance assessment and analyze the experimental conditions, parameter variables, and application scope based on force plate technology. As there is a wide range of balance assessment tests and a variety of commercial force plate systems to choose from, there is room for further improvement of the test details and evaluation variables of the balance assessment. The recommendations presented in this article are the foundation and key part of the postural balance assessment; these recommendations focus on the type of force plate, the subject's foot posture, and the choice of assessment variables, which further enriches the content of posturography. In order to promote a more reasonable balance assessment method based on force plates, further methodological research and a stronger consensus are still needed.

GADF/GASF-HOG:feature extraction methods for hand movement classification from surface electromyography.

Objective: Human intention gesture recognition is widely used in hand rehabilitation, artificial limb control, teleoperation, human-computer interaction and other fields. It has great application value, however, how to extract human intention gesture accurately has been a research hotspot. Approach: Inspired by the image processing technology of machine vision, the surface electromyographic (sEMG) signal was selected as the source signal of motion intention in this work, and the original sEMG signal was converted into Gramian Angular Summation/Difference Field (GASF/GADF) image. Then, Histogram of Oriented Gradient (HOG) features of the corresponding GADF and GASF image were extracted. The extracted features are named as GASF-HOG and GADF-HOG. The Bagging method was used to map the features to six common gestures to realize the classification of intention gestures. Ten volunteers participated in the experiment, and the experimental data were used to verify the proposed method. Main results: The experimental results showed that the average accuracies of the proposed methods (GADF-HOG with Bagging, GASF-HOG with Bagging) were as follow: GADF-HOG with Bagging was with 95.73 ± 1.90%, and GASF-HOG with Bagging was with 93.63 ± 1.54%. Significance: The method proposed in this paper is inspired by image processing technology of machine vision, which provides a new idea about the human intention gesture recognition by combining the interdisciplinary knowledge.

VMD-based denoising methods for surface electromyography signals.

OBJECTIVE: Since noise is inevitably introduced during the measurement process of surface electromyographic (sEMG) signals, two novel methods for denoising based on the variational mode decomposition (VMD) method were proposed in this work. Prior to this study, there has been no literature relating to how VMD is applied to sEMG denoising. APPROACH: The first proposed method uses the VMD method to decompose the signal into multiple variational mode functions (VMFs), each of which has its own center frequency and narrow band, and then the wavelet soft thresholding (WST) method is applied to each VMF. This method is termed the VMD-WST. The second proposed method uses the VMD method to decompose the signal into multiple VMFs, and then the soft interval thresholding (SIT) method is performed on each VMF, which is abbreviated as VMD-SIT. Ten healthy subjects and ten stroke patients participated in the experiment, and the sEMG signals of bicep brachii were measured and analyzed. In this paper, three methods are used for quantitative evaluation of the filtering performance: the signal-to-noise ratio (SNR), root mean square error and R-squared value. The proposed two methods (VMD-WST, VMD-SIT) are compared with the empirical mode decomposition (EMD) method and the wavelet method. MAIN RESULTS: The experimental results showed that the VMD-WST and VMD-SIT methods can effectively filter the noise effect, and the denoising effects were better than the EMD method and the wavelet method. The VMD-SIT method has the best performance. SIGNIFICANCE: This study provides a new means of eliminating the noise of sEMG signals based on the VMD method, and it can be applied in the fields of limb movement classification, disease diagnosis, human-machine interaction and so on.

Proportional myoelectric and compensating control of a cable-conduit mechanism-driven upper limb exoskeleton.

Recent studies have indicated that human motion recognition based on surface electromyography (sEMG) is a reliable and natural method for achieving motion intention. However, achieving accurate estimates of intended motion using a low computational cost is the main challenge in this scenario. In this study, a proportional myoelectric and compensating control method for estimating and assisting human motion intention with a cable-conduit mechanism-driven upper limb exoskeleton was proposed. The integral signal of sEMG and its time-delayed signals were applied as a new feature vector to represent the role of sEMG, which ensured the accuracy and real-time performance of motion estimation. An integrated circuit was used to reduce time of feature extraction. A feed-forward compensator was designed to compensate for the effect of the hysteresis problem in the exoskeleton, which is inevitable when the cable-conduit mechanism was applied to reduce the exoskeleton weight. The model-free control method based on PID method and least squares support vector machine were applied to avoid calculating the complex biomechanical model of human upper limb and the dynamic model of exoskeleton. Experimental results validated the proposed method. The average values of the root-mean-square difference (RMSD) for motion estimation were 0.0579 ± 0.0085 [motion with constant pace (CP)] and 0.0845 ± 0.0137 [motion with variable pace (VP)]. The Bland-Altman analysis results showed that the estimated angle of the proposed method was consistent with the actual angle. The performance of the control method was good, and the accuracies were 98.5608% ± 0.4485% (motion with CP) and 96.6119% ± 0.6628% (motion with VP).

Detection of stretch reflex onset based on empirical mode decomposition and modified sample entropy.

BACKGROUND: Accurate spasticity assessment provides an objective evaluation index for the rehabilitation treatment of patients with spasticity, and the key is detecting stretch reflex onset. The surface electromyogram of patients with spasticity is prone to false peaks, and its data length is unstable. These conditions decrease signal differences before and after stretch reflex onset. Therefore, a method for detecting stretch reflex onset based on empirical mode decomposition denoising and modified sample entropy recognition is proposed in this study. RESULTS: The empirical mode decomposition algorithm is better than the wavelet threshold algorithm in denoising surface electromyogram signal. Without adding Gaussian white noise to the electromyogram signal, the stretch reflex onset recognition rate of the electromyogram signal before and after empirical mode decomposition denoising was increased by 56%. In particular, the recognition rate of stretch reflex onset under the optimal parameter of the modified sample entropy can reach up to 100% and the average recognition rate is 93%. CONCLUSIONS: The empirical mode decomposition algorithm can eliminate the baseline activity of the surface electromyogram signal before stretch reflex onset and effectively remove noise from the signal. The identification of stretch reflex onset using combined empirical mode decomposition and modified sample entropy is better than that via modified sample entropy alone, and stretch reflex onset can be accurately determined.

Design of a wearable cable-driven upper limb exoskeleton based on epicyclic gear trains structure.

BACKGROUND: Many countries, including Japan, Italy, and China are experiencing demographic shifts as their populations age. Some basic activities of daily living (ADLs) are difficult for elderly people to complete independently due to declines in motor function. OBJECTIVE: In this paper, a 6-DOF wearable cable-driven upper limb exoskeleton (CABexo) based on epicyclic gear trains structure is proposed. METHODS: The main structure of the exoskeleton system is composed of three epicyclic gear train sections. This new exoskeleton has a parallel mechanical structure to the traditional serial structure, but is stiffer and has a stronger carrying capacity. The traditional gear transmission structure is replaced with a cable transmission system, which is quieter, and has higher accuracy and smoother transmission. RESULTS AND CONCLUSIONS: The static workspace of the exoskeleton is large enough to meet the demand of assisting aged and disabled individuals in completing most of their activities of daily living (ADLs).

Research on musculoskeletal model of elbow joint for evaluating the feasibility of FES.

Tremor usually occurs in a patient's upper limb with a roughly sinusoidal profile. Understanding the inner mechanism of the involuntary movement is fundamental to improving tremor suppression treatments. Therefore, the musculoskeletal model of the elbow joint was developed in this study. Initially, healthy subjects were selected to simulate tremor and the tremulous data was collected with the purpose of sparing patients from fatigue. With the recorded joint angle and surface EMG (sEMG), the model was calibrated to subjects by optimization approach. The activation derived from the electric pulse was employed to drive the tuned model and the model's output was compared with the angle predicted by the EMG-driven musculoskeletal model. The results demonstrated that the performance of the calibrated model was improved by a smaller normalized root mean square error and a higher coefficient of determination compared with the no-tuned model. There was no significant difference between the angles estimated by the tuned model activated by the electric pulse and muscle excitation. It indicates that neural activation could be replaced by the electric pulse to excite the limbs for desired angle. Therefore, the study presents a good way to evaluate the feasibility of Functional Electric Stimulation to suppress tremor.