Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.
Based on the structure and motion bionic principle of the normal adult fingers, biological characteristics of human hands were analyzed, and a wearable exoskeleton hand function training device for the rehabilitation of stroke patients or patients with hand trauma was designed. This device includes the exoskeleton mechanical structure and the electromyography (EMG) control system. With adjustable mechanism, the device was capable to fit different finger lengths, and by capturing the EMG of the users’ contralateral limb, the motion state of the exoskeleton hand was controlled. Then driven by the device， the user’s fingers conducting adduction/abduction rehabilitation training was carried out. Finally, the mechanical properties and training effect of the exoskeleton hand were verified through mechanism simulation and the experiments on the experimental prototype of the wearable exoskeleton hand function training device.
This study investigated the effect of prolonged walking with load carriage on body posture, muscle fatigue, heart rate and blood pressure of the tested subjects. Ten healthy volunteers performed 30 min walking trials on treadmill (speed=1.1 m/s) with different backpack loads [0% body weight (BW), 10%BW, 15%BW and 20%BW]. The change of body posture, muscle fatigue, heart rate and blood pressure before and after walking and the recovery of muscle fatigue during the rest time (0, 5, 10 and 15 min) were collected using the Bortec AMT-8 and the NDI Optotrak Certus. Results showed that the forward trunk and head angle, muscle fatigue, heart rate and blood pressure increased with the increasing backpack loads and bearing time. With the 20%BW load, the forward angle, muscle fatigue and systolic pressure were significantly higher than with lighter weights. No significantly increased heart rate and diastolic pressure were found. Decreased muscle fatigue was found after removing the backpack in each load trial. But the recovery of the person with 20%BW load was slower than that of 0%BW,10%BW and 15%BW. These findings indicated that the upper limit of backpack loads for college-aged students should be between 15% BW and 20%BW according to muscle fatigue and forward angle. It is suggested that backpack loads should be restricted to no more than 15%BW for walks of up to 30 min duration to avoid irreversible muscle fatigue.
To better evaluate neuromuscular function of patients with stroke related motor dysfunction, we proposed an effective corticomuscular coherence analysis and coherent significant judgment method. Firstly, the related functional frequency bands in the electroencephalogram (EEG) were extracted via wavelet decomposition. Secondly, coherence were analysed between surface electromyography (sEMG) and sub-bands extracted from EEG. Further more, a coherent significant indicator was defined to quantitatively describe the similarity in certain frequency domain and phase lock activity between EEG and sEMG. Through the analysis of corticomuscular coherence during knee flexion-extension of stroke patients and healthy controls, we found that the stroke patients exhibited significantly lower gamma-band corticomuscular coherence in performing the task with their affected leg, and there was no statistically significant difference between their unaffected lag and the healthy controls, but with the rehabilitation training, the bilateral difference of corticomuscular coherence in patients decreased gradually.
In order to solve the problems of insufficient stimulation channels and lack of stimulation effect feedback in the current electrical stimulation system, a functional array electrode electrical stimulation system with surface electromyography (sEMG) feedback was designed in this paper. Firstly, the effectiveness of the system was verified through in vitro and human experiments. Then it was confirmed that there were differences in the number of amperage needed to achieve the same stimulation stage among individuals, and the number of amperage required by men was generally less than that of women. Finally, it was verified that the current required for square wave stimulation was smaller than that for differential wave stimulation if the same stimulation stage was reached. This system combined the array electrode and sEMG feedback to improve the accuracy of electrical stimulation and performed the whole process recording of feedback sEMG signal in the process of electrical stimulation, and the electrical stimulation parameters could change with the change of the sEMG signal. The electrical stimulation system and sEMG feedback worked together to form a closed-loop electrical stimulation working system, so as to improve the efficiency of electrical stimulation rehabilitation treatment. In conclusion, the functional array electrode electrical stimulation system with sEMG feedback developed in this paper has the advantages of simple operation, small size and low power consumption, which lays a foundation for the introduction of electrical stimulation rehabilitation treatment equipment into the family, and also provides certain reference for the development of similar products in the future.
This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals, as well as the low recognition rates of conventional classifiers. A novel gesture recognition approach was proposed, which transformed surface electromyography signals into grayscale images and employed convolutional neural networks as classifiers. The method began by segmenting the active portions of the surface electromyography signals using an energy threshold approach. Temporal voltage values were then processed through linear scaling and power transformations to generate grayscale images for convolutional neural network input. Subsequently, a multi-view convolutional neural network model was constructed, utilizing asymmetric convolutional kernels of sizes 1 × n and 3 × n within the same layer to enhance the representation capability of surface electromyography signals. Experimental results showed that the proposed method achieved recognition accuracies of 98.11% for 13 gestures and 98.75% for 12 multi-finger movements, significantly outperforming existing machine learning approaches. The proposed gesture recognition method, based on surface electromyography grayscale images and multi-view convolutional neural networks, demonstrates simplicity and efficiency, substantially improving recognition accuracy and exhibiting strong potential for practical applications.
The aim of this study was to design a simple, economic, with high Common Mode Rejection Ratio (CMRR), preamplifier and multi-channel masticatory muscle surface electromyography (sEMG) signal acquisition system assisting to diagnose temporomandibular disorders (TMD). We used the USB interface technology in the EMG data with the aid of the windows to operate system and graphical interface. Eight patients with TMD and eight controls were analyzed separately using this system. In this system, we analyzed sEMG by an optional combination of time domain, frequency domain, time-frequency, several spectral analysis, wavelets and other special algorithms under multi-parameter. Multi-channel sEMG System of Masticatory Muscles is a simple, economic system. It has high sensitivity and specificity. The sEMG signals were changed in patients with TMD. The system would pave the way for diagnosis TMD and help us to assess the treatment effect. A novel and objective method is provided for diagnosis and treatment of oral-maxillofacial disease and functional reconstruction.
Surface electromyography (sEMG) is a weak signal which is non-stationary and non-periodic. The sEMG classification methods based on time domain and frequency domain features have low recognition rate and poor stability. Based on the modeling and analysis of sEMG energy kernel, this paper proposes a new method to recognize human gestures utilizing convolutional neural network (CNN) and phase portrait of sEMG energy kernel. Firstly, the matrix counting method is used to process the sEMG energy kernel phase portrait into a grayscale image. Secondly, the grayscale image is preprocessed by moving average method. Finally, CNN is used to recognize sEMG of gestures. Experiments on gesture sEMG signal data set show that the effectiveness of the recognition framework and the recognition method of CNN combined with the energy kernel phase portrait have obvious advantages in recognition accuracy and computational efficiency over the area extraction methods. The algorithm in this paper provides a new feasible method for sEMG signal modeling analysis and real-time identification.
Surface electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart. In this paper, an endpoint-detection algorithm which is insensitive to ECG interference is proposed. In the algorithm, endpoints of sEMG are detected based on the short-time energy and short-time zero-crossing rates of sEMG. The thresholds of short-time energy and short-time zero-crossing rate are set according to the statistical difference of short-time zero-crossing rate between sEMG and ECG, and the statistical difference of short-time energy between sEMG and the background noise. Experiment results on the sEMG of rectus abdominis muscle demonstrate that the algorithm detects the endpoints of the sEMG with a high accuracy rate of 95.6%.
Thymoma complicated with polymyositis and myasthenia gravis is a rare case, which can be clearly diagnosed and given symptomatic treatment according to its own diagnostic criteria, imaging and laboratory examinations. This paper reports the clinical data of a thymoma patient with polymyositis and myasthenia gravis admitted to the Seventh Affiliated Hospital of Sun Yat-Sen University, and discusses the possible pathogenesis and treatment methods.