EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees
We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of fre...
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| Published in: | Journal of neuroengineering and rehabilitation Vol. 9; no. 1; p. 42 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
28.06.2012
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1743-0003, 1743-0003 |
| Online Access: | Get full text |
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| Abstract | We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% ± 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% ± 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees. |
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| AbstractList | We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% [+ or -] 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% [+ or -] 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees. We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% ± 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% ± 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees. : We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% plus or minus 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% plus or minus 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees. We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% ± 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% ± 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees.We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% ± 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% ± 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees. Abstract We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% ± 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% ± 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees. Doc number: 42 Abstract: We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% ± 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% ± 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees. |
| ArticleNumber | 42 |
| Audience | Academic |
| Author | Muceli, Silvia Jiang, Ning Vest-Nielsen, Johnny LG Farina, Dario |
| AuthorAffiliation | 1 Strategic Technology Management, OttoBock HealthCare GmbH, Max-Näder Str. 15, Duderstadt, 37115, Germany 3 Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, , Von-Sieblod-str. 4, Göttingen, 37075, Germany 2 Center for Sensory-Motor Interaction (SMI), Dept. of Health Science and Technology (HST), Fredrik Bajers Vej 7 D3, Aalborg University, Aalborg, 9220, Denmark |
| AuthorAffiliation_xml | – name: 1 Strategic Technology Management, OttoBock HealthCare GmbH, Max-Näder Str. 15, Duderstadt, 37115, Germany – name: 2 Center for Sensory-Motor Interaction (SMI), Dept. of Health Science and Technology (HST), Fredrik Bajers Vej 7 D3, Aalborg University, Aalborg, 9220, Denmark – name: 3 Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, , Von-Sieblod-str. 4, Göttingen, 37075, Germany |
| Author_xml | – sequence: 1 givenname: Ning surname: Jiang fullname: Jiang, Ning organization: , OttoBock HealthCare GmbH, Strategic Technology Management, Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience,University Medical Center Göttingen, Georg-August University – sequence: 2 givenname: Johnny LG surname: Vest-Nielsen fullname: Vest-Nielsen, Johnny LG organization: Center for Sensory-Motor Interaction (SMI), Dept. of Health Science and Technology (HST), Aalborg University – sequence: 3 givenname: Silvia surname: Muceli fullname: Muceli, Silvia organization: Center for Sensory-Motor Interaction (SMI), Dept. of Health Science and Technology (HST), Aalborg University, Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience,University Medical Center Göttingen, Georg-August University – sequence: 4 givenname: Dario surname: Farina fullname: Farina, Dario email: dario.farina@bccn.uni-goettingen.de organization: Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience,University Medical Center Göttingen, Georg-August University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22742707$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Jiang et al.; licensee BioMed Central Ltd. 2012 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. COPYRIGHT 2012 BioMed Central Ltd. 2012 Jiang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2012 Jiang et al.; licensee BioMed Central Ltd. 2012 Jiang et al.; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Jiang et al.; licensee BioMed Central Ltd. 2012 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: COPYRIGHT 2012 BioMed Central Ltd. – notice: 2012 Jiang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright ©2012 Jiang et al.; licensee BioMed Central Ltd. 2012 Jiang et al.; licensee BioMed Central Ltd. |
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| Keywords | Training Scenario Intact Side Myoelectric Control Joint Angle Control Group Group |
| Language | English |
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| SubjectTerms | Accuracy Active control Adult Algorithms Amputation Amputation, Traumatic - physiopathology Amputees Arm Artificial Limbs Automatic Data Processing Biomechanical Phenomena Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Classification Control algorithms Electrodes Electromyography Estimating Female Functional Laterality - physiology Hand - physiology Humans Implants, Artificial Joints - anatomy & histology Kinematics Male Middle Aged Myoelectric control Neural networks Neural Networks (Computer) Neurology Neurosciences Prostheses Prosthesis Radius Rehabilitation Medicine Studies Surface chemistry Wrist Wrist - physiology |
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