scholarly journals Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4213
Author(s):  
Chengjun Chen ◽  
Kai Huang ◽  
Dongnian Li ◽  
Zhengxu Zhao ◽  
Jun Hong
Keyword(s):  
Surface Electromyography ◽  
Network Models ◽  
Semg Signal ◽  
Tightening Torque ◽  
Torque Estimation ◽  
Monitoring Accuracy ◽  
Torque Transducer ◽  
Bolt Connection ◽  
Product Assembly ◽  
Semg Signals

The precise application of tightening torque is one of the important measures to ensure accurate bolt connection and improvement in product assembly quality. Currently, due to the limited assembly space and efficiency, a wrench without the function of torque measurement is still an extensively used assembly tool. Therefore, wrench torque monitoring is one of the urgent problems that needs to be solved. This study proposes a multi-segmentation parallel convolution neural network (MSP-CNN) model for estimating assembly torque using surface electromyography (sEMG) signals, which is a method of torque monitoring through classification methods. The MSP-CNN model contains two independent CNN models with different or offset torque granularities, and their outputs are fused to obtain a finer classification granularity, thus improving the accuracy of torque estimation. First, a bolt tightening test bench is established to collect sEMG signals and tightening torque signals generated when the operator tightens various bolts using a wrench. Second, the sEMG and torque signals are preprocessed to generate the sEMG signal graphs. The range of the torque transducer is divided into several equal subdivision ranges according to different or offset granularities, and each subdivision range is used as a torque label for each torque signal. Then, the training set, verification set, and test set are established for torque monitoring to train the MSP-CNN model. The effects of different signal preprocessing methods, torque subdivision granularities, and pooling methods on the recognition accuracy and torque monitoring accuracy of a single CNN network are compared experimentally. The results show that compared to maximum pooling, average pooling can improve the accuracy of CNN torque classification and recognition. Moreover, the MSP-CNN model can improve the accuracy of torque monitoring as well as solve the problems of non-convergence and slow convergence of independent CNN network models.

scholarly journals Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1036
Author(s):  
Fuyuan Liao ◽  
Xueyan Zhang ◽  
Chunmei Cao ◽  
Isabella Yu-Ju Hung ◽  
Yanni Chen ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Biceps Brachii ◽  
Cupping Therapy ◽  
Nonlinear Method ◽  
Muscle Recovery ◽  
Sham Control ◽  
Semg Signal ◽  
Relative Change ◽  
Semg Signals

This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (−300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant’s biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (Ems) algorithm. When exercise was performed immediately or 24 h after sham control, Ems of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, Ems also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, Ems did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by Ems is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

scholarly journals Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors

2021 ◽  
Vol 7 ◽  
pp. e448
Author(s):  
Yongfei Feng ◽  
Mingwei Zhong ◽  
Xusheng Wang ◽  
Hao Lu ◽  
Hongbo Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Surface Electromyography ◽  
Hand Movement ◽  
Semg Signal ◽  
Signal Waveform ◽  
Bp Neural Network Model ◽  
Semg Signals

The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient’s hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human’s arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues’ amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient’s hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.

scholarly journals Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Application in Neurorobotics

2021 ◽  
Author(s):  
Tianyun Sun ◽  
Qin Hu ◽  
Jacqueline Libby ◽  
S. Farokh Atashzar
Keyword(s):  
Surface Electromyography ◽  
Complex Model ◽  
High Density ◽  
Transient Phase ◽  
Spatiotemporal Resolution ◽  
Semg Signal ◽  
Training Time ◽  
Motor Intention ◽  
Density Surface ◽  
Human Robot Interfaces

Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, deepnets are generally challenged by long training times (affecting the practicality and calibration), complex model architectures (affecting the predictability of the outcomes), a large number of trainable parameters (increasing the need for big data), and possibly overfitting. Capitalizing on our recent work on homogeneous temporal dilation in a Recurrent Neural Network (RNN) model, this paper proposes, for the first time, heterogeneous temporal dilation in an LSTM model and applies that to high-density surface electromyography (HD-sEMG), allowing for decoding dynamic temporal dependencies with tunable temporal foci. In this paper, a 128-channel HD-sEMG signal space is considered due to the potential for enhancing the spatiotemporal resolution of human-robot interfaces. Accordingly, this paper addresses a challenging motor intention decoding problem of neurorobots, namely, transient intention identification. The aforementioned problem only takes into account the dynamic and transient phase of gesture movements when the signals are not stabilized or plateaued, addressing which can significantly enhance the temporal resolution of human-robot interfaces. This would eventually enhance seamless real-time implementations. Additionally, this paper introduces the concept of dilation foci to modulate the modeling of temporal variation in transient phases. In this work a high number (i.e. 65) of gestures is included, which adds to the complexity and significance of the understudied problem. Our results show state-of-the-art performance for gesture prediction in terms of accuracy, training time, and model convergence.

scholarly journals Implementation of a real-time automatic onset time detection for surface electromyography measurement systems using NI myRIO

2016 ◽  
Vol 20 (suppl. 2) ◽  
pp. 591-602 ◽  
Author(s):  
Chaiwat Lersviriyanantakul ◽  
Apidet Booranawong ◽  
Kiattisak Sengchuai ◽  
Pornchai Phukpattaranont ◽  
Booncharoen Wongkittisuksa ◽  
...  
Keyword(s):  
Real Time ◽  
Surface Electromyography ◽  
Onset Time ◽  
Simple Algorithm ◽  
Algorithm Analysis ◽  
The Real ◽  
Measurement Systems ◽  
Smoothing Process ◽  
The Given ◽  
Semg Signals

For using surface electromyography (sEMG) in various applications, the process consists of three parts: an onset time detection for detecting the first point of movement signals, a feature extraction for extracting the signal attribution, and a feature classification for classifying the sEMG signals. The first and the most significant part that influences the accuracy of other parts is the onset time detection, particularly for automatic systems. In this paper, an automatic and simple algorithm for the real-time onset time detection is presented. There are two main processes in the proposed algorithm; a smoothing process for reducing the noise of the measured sEMG signals and an automatic threshold calculation process for determining the onset time. The results from the algorithm analysis demonstrate the performance of the proposed algorithm to detect the sEMG onset time in various smoothing-threshold equations. Our findings reveal that using a simple square integral (SSI) as the smoothing-threshold equation with the given sEMG signals gives the best performance for the onset time detection. Additionally, our proposed algorithm is also implemented on a real hardware platform, namely NI myRIO. Using the real-time simulated sEMG data, the experimental results guarantee that the proposed algorithm can properly detect the onset time in the real-time manner.

Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network

2020 ◽  
Vol 32 (4) ◽  
pp. 741-758
Author(s):  
Xugang Xi ◽  
Wenjun Jiang ◽  
Seyed M. Miran ◽  
Xian Hua ◽  
Yun-Bo Zhao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Surface Electromyography ◽  
Contractile Activity ◽  
Random Network ◽  
Principal Component ◽  
Small World ◽  
Support Vector ◽  
Echo State Network ◽  
Better Than ◽  
Semg Signals

Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals. In this letter, we propose a feature extraction method of sEMG signals based on the improved small-world leaky echo state network (ISWLESN). The reservoir of leaky echo state network (LESN) is connected by a random network. First, we improved the reservoir of the echo state network (ESN) by these networks and used edge-added probability to improve these networks. That idea enhances the adaptability of the reservoir, the generalization ability, and the stability of ESN. Then we obtained the output weight of the network through training and used it as features. We recorded the sEMG signals during different activities: falling, walking, sitting, squatting, going upstairs, and going downstairs. Afterward, we extracted corresponding features by ISWLESN and used principal component analysis for dimension reduction. At the end, scatter plot, the class separability index, and the Davies-Bouldin index were used to assess the performance of features. The results showed that the ISWLESN clustering performance was better than those of LESN and ESN. By support vector machine, it was also revealed that the performance of ISWLESN for classifying the activities was better than those of ESN and LESN.

Classification of Muscle Fatigue in Dynamic Contraction Using Surface Electromyography Signals and Multifractal Singularity Spectral Analysis

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Kiran Marri ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Nearest Neighbor ◽  
Biceps Brachii ◽  
Information Gain ◽  
Multifractal Spectrum ◽  
Singularity Spectrum ◽  
K Nearest Neighbor ◽  
Dynamic Contraction ◽  
Semg Signals

Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series.

Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

2019 ◽  
Vol 14 (2) ◽  
pp. 158-164 ◽  
Author(s):  
G. Emayavaramban ◽  
A. Amudha ◽  
T. Rajendran ◽  
M. Sivaramkumar ◽  
K. Balachandar ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Network Models ◽  
Prosthetic Hand ◽  
Neural Network Models ◽  
The Neural Network ◽  
Semg Signals

Background: Identifying user suitability plays a vital role in various modalities like neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper analysis the user suitability based on neural networks (NN), subjects, age groups and gender for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand. Six parametric feature extraction algorithms are used to extract the features from sEMG signals such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural Network. Methods: sEMG signals generated from forearm muscles of the participants are collected through an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the user is identified in the sEMG recognition module using signal processing, feature extraction and machine learning techniques. The information about the identified movement is passed to microcontroller wherein a control is developed to command the prosthetic hand to emulate the identified movement. Results: From the six feature extraction algorithms and two neural network models used in the study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best suited for this study as the neural network model is specially designed for pattern matching problem. Moreover, it has simple architecture and low computational complexity. AR Burg is found to be the best feature extraction technique in this study due to its high resolution for short data records and its ability to always produce a stable model. In all the neural network models, the maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are best suited for the study due to their better muscle contractions. Better muscle fatigue resistance has contributed for better performance of female subjects as compared to male subjects. From the single trial analysis, it can be observed that the hand close movement has achieved best recognition rate for all neural network models. Conclusion: In this paper a study was conducted to identify user suitability for designing hand prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements. The suitability of the user was identified using two neural networks with six parametric features. From the result, it was concluded thatfit women doing regular physical exercises aged between 26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition Neural Network with AR Burg extraction features using extension movements will be a better way to design the HMI. However, Signal acquisition based on wireless method is worth considering for the future.

scholarly journals Design of Mirror Therapy System Base on Multi-Channel Surface-Electromyography Signal Pattern Recognition and Mobile Augmented Reality

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2142
Author(s):  
Lizheng Liu ◽  
Jianjun Cui ◽  
Jian Niu ◽  
Na Duan ◽  
Xianjia Yu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Augmented Reality ◽  
Surface Electromyography ◽  
Hand Movement ◽  
Low Noise ◽  
Support Vector ◽  
Mobile Augmented Reality ◽  
Mirror Therapy ◽  
Semg Signal ◽  
Signal Pattern

Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and so on. However, current signal analysis methods still fail to achieve accurate recognition of multimode motion in a very reliable way due to the weak physiological signal and low noise-ratio. In this paper, a mirror therapy system based on multi-channel SEMG signal pattern recognition and mobile augmented reality is studied. Besides, wavelet transform method is designed to mitigate the noise. The spectrogram obtained by analyzing electromyography signals is proposed to be used as an image. Two approaches, including Convolutional Neural Network (CNN) and grid-optimized Support Vector Machine (SVM), are designed to classify the SEMG of different gestures. The mobile augmented reality provides a virtual hand movement in the real environment to perform mirror therapy process. The experimental results show that the overall accuracy of SVM is 93.07%, and that of CNN is up to 97.8%.

Sign in / Sign up

Export Citation Format

Share Document