Semg Based Recognition Of Hand Motions For Lower Limb Prostheses

2021 ◽  
Vol 16 ◽  
Author(s):  
Keerti Rajput ◽  
Karan Veer
Keyword(s):  
Lower Limb ◽  
Confusion Matrix ◽  
Support Vector ◽  
Semg Signal ◽  
Linear Discriminant ◽  
Network Support ◽  
Recorded Data ◽  
Non Stationary Signal ◽  
Semg Signals ◽  
Lower Limb Prostheses

Aim: On multiple muscle locations, surface electromyography (sEMG) signals were recorded to predict the effect of different hand movements. Background: Myoelectric information is a non-stationary signal, so extracting correct features is important to boost any myoelectric control devices' performance. The myoelectric signal is an electrical activity recorded by a surface electrode at various movements of the muscles. Objective: The study presented pattern recognition classification methods to select an excellent algorithm for controlling the SEMG signal. Method: Various time domain and frequency domain parameters were extracted prior to conduct the classifier test. Result: For the evaluation of the results for the recorded data (of all six movements), confusion matrix for neural network, support vector machine (SVM), DT, and linear discriminant analysis (LDA) classifiers is presented. Conclusion: This present study will be a step in analyzing different problems for developing lower limb prostheses.

scholarly journals A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

2020 ◽  
Vol 10 (8) ◽  
pp. 2638 ◽  
Author(s):  
Shuo Gao ◽  
Yixuan Wang ◽  
Chaoming Fang ◽  
Lijun Xu
Keyword(s):  
Machine Learning ◽  
Ground Reaction Force ◽  
Lower Limb ◽  
Confusion Matrix ◽  
Reaction Force ◽  
Simple System ◽  
Support Vector ◽  
Terrain Classification ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Automatic terrain classification in lower limb rehabilitation systems has gained worldwide attention. In this field, a simple system architecture and high classification accuracy are two desired attributes. In this article, a smart neuromuscular–mechanical fusion and machine learning-based terrain classification technique utilizing only two electromyography (EMG) sensors and two ground reaction force (GRF) sensors is reported for classifying three different terrains (downhill, level, and uphill). The EMG and GRF signals from ten healthy subjects were collected, preprocessed and segmented to obtain the EMG and GRF profiles in each stride, based on which twenty-one statistical features, including 9 GRF features and 12 EMG features, were extracted. A support vector machine (SVM) machine learning model is established and trained by the extracted EMG features, GRF features and the fusion of them, respectively. Several methods or statistical metrics were used to evaluate the goodness of the proposed technique, including a paired-t-test and Kruskal–Wallis test for correlation analysis of the selected features and ten-fold cross-validation accuracy, confusion matrix, sensitivity and specificity for the performance of the SVM model. The results show that the extracted features are highly correlated with the terrain changes and the fusion of the EMG and GRF features produces the highest accuracy of 96.8%. The presented technique allows simple system construction to achieve the precise detection of outcomes, potentially advancing the development of terrain classification techniques for rehabilitation.

scholarly journals Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

2022 ◽  
Author(s):  
Chiako Mokri ◽  
Mahdi Bamdad ◽  
Vahid Abolghasemi
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Muscle Force ◽  
Machine Learning Techniques ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Support Vector ◽  
Rehabilitation Robot ◽  
Learning Techniques ◽  
Semg Signals

AbstractThe main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.

Research On Exercise Fatigue Estimation Method of Pilates Rehabilitation Based On ECG and sEMG Feature Fusion

2021 ◽  
Author(s):  
Dujuan Li ◽  
Caixia Chen
Keyword(s):  
Feature Fusion ◽  
Recognition Rate ◽  
Estimation Method ◽  
Classification Model ◽  
Support Vector ◽  
Ecg Signal ◽  
Data Types ◽  
Semg Signal ◽  
Linear Discriminant ◽  
Fatigue Estimation

Abstract Purpose. Fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury in Pilates rehabilitation. Surface electromyography (sEMG) is used to estimate fatigue with low and unstable recognition rates. To improve the rate, this paper fused electrocardiogram (ECG) signal and sEMG signal under three different states, and the classification model of the improved proved particle swarm optimization support vector machine (IPSO-SVM) algorithm was established. Methods. Twenty subjects performed 150 minutes of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. After necessary preprocessing, the IPSO-SVM classification model based on feature fusion was established to identify three different fatigue states (relaxed, transition, and tired). The model effects of different classification algorithms and different fused data types were compared. Results. Compared with common physiological signal classification methods such as BP neural network algorithm(BPNN), K-nearest neighbor(KNN), and Linear discriminant analysis(LDA), IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion. The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation.

scholarly journals Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation

2019 ◽  
Vol 9 (14) ◽  
pp. 2795 ◽  
Author(s):  
Ahmed Baraka ◽  
Heba Shaban ◽  
Mohamad Abou El-Nasr ◽  
Omneya Attallah
Keyword(s):  
Confusion Matrix ◽  
Wearable Sensors ◽  
Human Locomotion ◽  
Measurement Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Tree Classifier ◽  
Different Types

Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biceps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of Parkinson’s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%.

scholarly journals Classifier Performance Evaluation in Wrist and Finger Movement Fitting Task Based on Forearm HD-sEMG

2021 ◽  
Author(s):  
Haiqiang Duan ◽  
Chenyun Dai ◽  
Wei Chen
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Hand Movement ◽  
The Other ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Superposition ◽  
Semg Signal ◽  
Linear Discriminant ◽  
Fitting In

Abstract Background: The transmission of human body movements to other devices through wearable smart bracelets have attracted more and more attentions in the field of human-machine interface (HMI) applications. However, due to the limitation of the collection range of wearable bracelets, it is necessary to study the relationship between the superposition of wrist and finger motion and their cooperative motion to simplify the collection system of the device.Methods: The multi-channel high-density surface electromyogram (HD-sEMG) signal has high spatial resolution and can improve the accuracy of multi-channel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement, and performed a linear fitting of the quantified features of fingers and wrist movements to verify the linear superposition relationship between fingers and wrist cooperative movements and their independent movements. The most important thing is to classify and predict the results of the fitting and the actual measured fingers and wrist cooperative actions by four commonly used classifiers: Linear Discriminant Analysis (LDA) ,K-Nearest Neighbor (KNN) ,Support Vector Machine (SVM) and Random Forest (RF), and evaluate the performance of the four classifiers in gesture fitting in detail according to the classification results.Results: In a total of 12 kinds of synthetic gesture actions, in the three cases where the number of fitting channels was selected as 8, 32 and 64, four classifiers of LDA, SVM, RF and KNN are used for classification prediction. When the number of fitting channels was 8, the prediction accuracy of LDA classifier was 99.70%, the classification accuracy of KNN was 99.40%, the classification accuracy of SVM was 99.20%, and the classification accuracy of RF was 93.75%. When the number of fitting channels was 32, the accuracy of LDA was 98.51%, the classification accuracy of KNN was 97.92%, the accuracy of SVM is 96.73%, and the accuracy of RF was 86.61%. When the number of fitting channels is 64, the accuracy of LDA is 95.83%, the classification accuracy of KNN is 91.67%, the accuracy of SVM is 86.90%, and the accuracy of RF is 83.30%.Conclusion: It can be seen from the results that when the number of fitting channels is 8, the classification accuracy of the three classifiers of LDA, KNN and SVM is basically the same, but the time-consuming of SVM is very small. When the amount of data is large, the priority should be selected SVM as the classifier. When the number of fitting channels increases, the classification accuracy of the LDA classifier will be higher than the other three classifiers, so the LDA classifier should be more appropriate. The classification accuracy of the RF classifier in this type of problem has always been far lower than the other three classifiers, so it is not recommended to use the RF classifier as a classifier for gesture stacking related work.

scholarly journals A Novel Adaptive Mutation PSO Optimized SVM Algorithm for sEMG-Based Gesture Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Le Cao ◽  
Wenyan Zhang ◽  
Xiu Kan ◽  
Wei Yao
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
Recognition Rate ◽  
Low Frequency ◽  
Adaptive Mutation ◽  
Support Vector ◽  
Semg Signal ◽  
Svm Algorithm ◽  
Svm Model ◽  
Semg Signals

In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG gesture recognition consisting of feature extraction, genetic algorithm (GA), and support vector machine (SVM) model is proposed. Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined. The AMPSO-SVM model based on combination processing is successfully applied to MYO bracelet dataset, and four gesture classifications are carried out. Furthermore, AMPSO-SVM is compared with PSO-SVM, GS-SVM, and BP. The sEMG gesture recognition rate of AMPSO-SVM is 0.975, PSO-SVM is 0.9463, GS-SVM is 0.9093, and BP is 0.9019. The experimental results show that AMPSO-SVM is effective for low-frequency sEMG signals of different gestures.

scholarly journals The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6291
Author(s):  
Lin Meng ◽  
Jun Pang ◽  
Ziyao Wang ◽  
Rui Xu ◽  
Dong Ming
Keyword(s):  
Data Fusion ◽  
Lower Limb ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Window Size ◽  
Linear Acceleration ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Acceleration Data

Locomotion recognition and prediction is essential for real-time human–machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classification models were built based on four machine learning methods—support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and linear discriminant analysis (LDA)—where a major vote strategy and a content constraint rule were utilized for improving the online performance of the classification decision. We compared four classifiers and further investigated the effect of data fusion on the online locomotion classification. The results showed that the SVM model with a sliding window size of 80 ms achieved the best recognition performance. The fusion of EMG signals does not only improve the recognition accuracy of steady-state locomotion activity from 90% (using acceleration data only) to 98% (using data fusion) but also enables the prediction of the next steady locomotion (∼370 ms). The study demonstrates that the employment of EMG in locomotion recognition could enhance online prediction performance.

scholarly journals Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 130 ◽  
Author(s):  
Yanxia Deng ◽  
Farong Gao ◽  
Huihui Chen
Keyword(s):  
Knee Joint ◽  
Lower Limb ◽  
Joint Angle ◽  
Pearson Correlation ◽  
Lower Limbs ◽  
Human Gait ◽  
Support Vector ◽  
Joint Movement ◽  
Knee Joint Angle ◽  
Semg Signals

Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis and rehabilitation medicine, sports medicine and other fields. This paper aims to improve the estimation accuracy and real-time performance, in the case of the knee joint angle in the lower limb, using a sEMG signal, in a proposed estimation algorithm of the continuous motion, based on the principal component analysis (PCA) and the regularized extreme learning machine (RELM). First, the sEMG signals, collected during the lower limb motion, are preprocessed, while feature samples are extracted from the acquired and preconditioned sEMG signals. Next, the feature samples dimensions are reduced by the PCA, as well as the knee joint angle system is measured by the three-dimensional motion capture system, are followed by the normalization of the feature variable value. The normalized sEMG feature is used as the input layer, in the RELM model, while the joint angle is used as the output layer. After training, the RELM model estimates the knee joint angle of the lower limbs, while it uses the root mean square error (RMSE), Pearson correlation coefficient and model training time as key performance indicators (KPIs), to be further discussed. The RELM, the traditional BP neural network and the support vector machine (SVM) estimation results are compared. The conclusions prove that the RELM method, not only has ensured the validity of results, but also has greatly reduced the learning train time. The presented work is a valuable point of reference for further study of the motion estimation in lower limb.

scholarly journals A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 259 ◽  
Author(s):  
Diana Toledo-Pérez ◽  
Miguel Martínez-Prado ◽  
Roberto Gómez-Loenzo ◽  
Wilfrido Paredes-García ◽  
Juvenal Rodríguez-Reséndiz
Keyword(s):  
Lower Limb ◽  
Classification Accuracy ◽  
Research Work ◽  
Principal Component ◽  
Support Vector ◽  
Semg Signal ◽  
Zero Crossings ◽  
The Difference ◽  
The Right

The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification.

scholarly journals MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

2021 ◽  
Vol 15 ◽  
Author(s):  
Kecheng Shi ◽  
Rui Huang ◽  
Zhinan Peng ◽  
Fengjun Mu ◽  
Xiao Yang
Keyword(s):  
Upper Limb ◽  
Lower Limb ◽  
Prediction Performance ◽  
Lower Limbs ◽  
Natural Interaction ◽  
Semg Signal ◽  
Good Movement ◽  
Lower Limb Movement ◽  
Feature Visualization ◽  
Semg Signals

The human–robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human–exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.

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