Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim
Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic public...
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| Published in: | Sensors (Basel, Switzerland) Vol. 22; no. 10; p. 3737 |
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| Main Authors: | , , , , |
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| Language: | English |
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| Abstract | Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods—the forward sequential selection method and the feature normalization method—were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results—the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis. |
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| AbstractList | Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human-computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods-the forward sequential selection method and the feature normalization method-were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results-the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis. Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human-computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods-the forward sequential selection method and the feature normalization method-were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results-the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis.Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human-computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods-the forward sequential selection method and the feature normalization method-were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results-the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis. |
| Audience | Academic |
| Author | Gonzalez-Navarro, Felix F. Lopez-Avitia, Roberto Bravo-Zanoguera, Miguel Reyna, M. A. Amezquita-Garcia, Jose |
| AuthorAffiliation | 3 Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico; fernando.gonzalez@uabc.edu.mx (F.F.G.-N.); mreyna@uabc.edu.mx (M.A.R.) 1 Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico; jose.amezquita@uabc.edu.mx (J.A.-G.); ravitia@uabc.edu.mx (R.L.-A.) 2 Ingeniería en Mecatrónica, Universidad Politécnica de Baja California, Mexicali 21376, Mexico |
| AuthorAffiliation_xml | – name: 2 Ingeniería en Mecatrónica, Universidad Politécnica de Baja California, Mexicali 21376, Mexico – name: 3 Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico; fernando.gonzalez@uabc.edu.mx (F.F.G.-N.); mreyna@uabc.edu.mx (M.A.R.) – name: 1 Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico; jose.amezquita@uabc.edu.mx (J.A.-G.); ravitia@uabc.edu.mx (R.L.-A.) |
| Author_xml | – sequence: 1 givenname: Jose orcidid: 0000-0002-8472-115X surname: Amezquita-Garcia fullname: Amezquita-Garcia, Jose – sequence: 2 givenname: Miguel orcidid: 0000-0003-1227-9726 surname: Bravo-Zanoguera fullname: Bravo-Zanoguera, Miguel – sequence: 3 givenname: Felix F. orcidid: 0000-0002-9627-676X surname: Gonzalez-Navarro fullname: Gonzalez-Navarro, Felix F. – sequence: 4 givenname: Roberto orcidid: 0000-0003-3615-6560 surname: Lopez-Avitia fullname: Lopez-Avitia, Roberto – sequence: 5 givenname: M. A. surname: Reyna fullname: Reyna, M. A. |
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| Cites_doi | 10.3103/S0735272719010047 10.1016/j.bspc.2020.101872 10.1109/ACCESS.2021.3129454 10.1155/2021/9194578 10.1109/EMBC.2015.7319780 10.1016/j.eswa.2020.113281 10.1016/j.eswa.2012.01.102 10.1002/mus.24631 10.1186/1743-0003-7-21 10.1016/j.jelekin.2019.07.008 10.1109/TBME.2007.912673 10.1016/j.medengphy.2018.04.007 10.1016/j.sna.2011.09.039 10.3389/fnbot.2019.00043 10.1016/j.patrec.2008.05.021 10.1016/j.procs.2018.07.012 10.1186/1743-0003-7-53 10.3390/ecsa-8-11262 10.1109/ISSBES.2015.7435877 10.1016/S1672-6529(16)60377-3 10.1016/0021-9290(93)90086-T 10.1016/j.neucom.2020.03.009 10.1109/TBME.1987.326033 10.1109/IEMBS.2007.4353640 10.1007/978-1-4615-5689-3 10.1109/ICARCV.2012.6485374 10.1016/j.neunet.2014.03.010 10.1002/9781119082934 |
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| Snippet | Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being... |
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| SubjectTerms | Analysis Artificial Limbs Bayes Theorem biomechanical simulation Classification classification model Electrodes Electromyography Electromyography - methods Humans Implants, Artificial Machine Learning Pattern Recognition, Automated - methods Prostheses Prosthesis Sensors Signal Processing, Computer-Assisted Technology application |
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| Title | Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim |
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