Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms
•This paper first reports using convolutional neural network (CNN) in shoulder muscle EMG signal processing, potentially to be used in upper arm exoskeleton motion control, clinical shoulder disorder diagnosis, clinical diagnosis of sport injury, evaluation of surgical treatment outcomes, time point...
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| Vydané v: | Computer methods and programs in biomedicine Ročník 197; s. 105721 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Ireland
Elsevier B.V
01.12.2020
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| ISSN: | 0169-2607, 1872-7565, 1872-7565 |
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| Abstract | •This paper first reports using convolutional neural network (CNN) in shoulder muscle EMG signal processing, potentially to be used in upper arm exoskeleton motion control, clinical shoulder disorder diagnosis, clinical diagnosis of sport injury, evaluation of surgical treatment outcomes, time points for athlete to return to the sports, as well as the assessment of stroke rehabilitation and improvement of activity of daily life.•Accuracy of CNN algorithm performance was accessed according to its performance in cross-subject, cross-device, cross-motion speed validation.•Results demonstrated multiple channel EMG signal processing using CNN algorithms is a highly effective methods for motion pattern recognition.
Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy.
A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained.
Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models.
The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy. |
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| AbstractList | •This paper first reports using convolutional neural network (CNN) in shoulder muscle EMG signal processing, potentially to be used in upper arm exoskeleton motion control, clinical shoulder disorder diagnosis, clinical diagnosis of sport injury, evaluation of surgical treatment outcomes, time points for athlete to return to the sports, as well as the assessment of stroke rehabilitation and improvement of activity of daily life.•Accuracy of CNN algorithm performance was accessed according to its performance in cross-subject, cross-device, cross-motion speed validation.•Results demonstrated multiple channel EMG signal processing using CNN algorithms is a highly effective methods for motion pattern recognition.
Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy.
A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained.
Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models.
The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy. Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy. A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained. Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models. The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy. Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy.BACKGROUND AND OBJECTIVESurface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy.A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained.METHODSA novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained.Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models.RESULTSResults showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models.The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy.CONCLUSIONThe EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy. |
| ArticleNumber | 105721 |
| Author | Ni, Guoxin Muh, Stephanie Zhang, Xiaodong Zhou, Yang Chen, Chaoyang Chen, Christine Lemos, Stephen Jiang, Yongyu |
| Author_xml | – sequence: 1 givenname: Yongyu orcidid: 0000-0002-1525-3997 surname: Jiang fullname: Jiang, Yongyu organization: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China – sequence: 2 givenname: Christine surname: Chen fullname: Chen, Christine organization: Department of Computer Science, College of Engineering, University of Michigan, Ann Arbor, USA – sequence: 3 givenname: Xiaodong surname: Zhang fullname: Zhang, Xiaodong email: xdzhang@mail.xjtu.edu.cn organization: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China – sequence: 4 givenname: Chaoyang surname: Chen fullname: Chen, Chaoyang email: cchen@wayne.edu organization: Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, USA – sequence: 5 givenname: Yang orcidid: 0000-0002-0501-1759 surname: Zhou fullname: Zhou, Yang organization: Robotic Rehabilitation Lab, Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA – sequence: 6 givenname: Guoxin surname: Ni fullname: Ni, Guoxin organization: Department of Rehabilitation Medicine, First Affiliated Hospital, Fujian Medical University, Fuzhou, China – sequence: 7 givenname: Stephanie orcidid: 0000-0001-6617-4116 surname: Muh fullname: Muh, Stephanie organization: Department of Orthopaedic Surgery, Henry Ford Health System, Detroit, MI, USA – sequence: 8 givenname: Stephen surname: Lemos fullname: Lemos, Stephen organization: Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32882593$$D View this record in MEDLINE/PubMed |
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| Keywords | Motion Convolutional neural network (CNN) Pattern recognition Electromyography (EMG) Shoulder Machine learning |
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| Snippet | •This paper first reports using convolutional neural network (CNN) in shoulder muscle EMG signal processing, potentially to be used in upper arm exoskeleton... Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however,... |
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| SubjectTerms | Algorithms Convolutional neural network (CNN) Electromyography Electromyography (EMG) Hand Humans Machine Learning Motion Movement Pattern recognition Shoulder |
| Title | Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms |
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