Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., cla...
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| Vydané v: | Sensors (Basel, Switzerland) Ročník 21; číslo 16; s. 5677 |
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| Abstract | Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively. |
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| AbstractList | Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively. Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre‐recorded datasets. While real‐time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real‐time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real‐time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real‐time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able‐bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other clas-sifiers, with an average classification accuracy of above 97%. On the other hand, the real‐time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively. Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively. Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively. |
| Author | Lindén, Maria GholamHosseini, Hamid Abbaspour, Sara Naber, Autumn Ortiz-Catalan, Max |
| AuthorAffiliation | 3 Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden 4 Operational Area 3, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden 6 Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand; hamid.gholamhosseini@aut.ac.nz 7 School of Innovation, Design and Engineering, Mälardalen University, 722 20 Västerås, Sweden; maria.linden@mdh.se 1 Department of Neurology, Massachusetts General Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, MA 02114, USA 2 Center for Bionics and Pain Research, 431 80 Möndal, Sweden; anaber@pm.me (A.N.); maxo@chalmers.se (M.O.-C.) 5 Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden |
| AuthorAffiliation_xml | – name: 2 Center for Bionics and Pain Research, 431 80 Möndal, Sweden; anaber@pm.me (A.N.); maxo@chalmers.se (M.O.-C.) – name: 5 Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden – name: 1 Department of Neurology, Massachusetts General Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, MA 02114, USA – name: 7 School of Innovation, Design and Engineering, Mälardalen University, 722 20 Västerås, Sweden; maria.linden@mdh.se – name: 6 Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand; hamid.gholamhosseini@aut.ac.nz – name: 3 Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden – name: 4 Operational Area 3, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden |
| Author_xml | – sequence: 1 givenname: Sara surname: Abbaspour fullname: Abbaspour, Sara – sequence: 2 givenname: Autumn orcidid: 0000-0002-8284-5503 surname: Naber fullname: Naber, Autumn – sequence: 3 givenname: Max surname: Ortiz-Catalan fullname: Ortiz-Catalan, Max – sequence: 4 givenname: Hamid surname: GholamHosseini fullname: GholamHosseini, Hamid – sequence: 5 givenname: Maria orcidid: 0000-0003-1940-1747 surname: Lindén fullname: Lindén, Maria |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55822$$DView record from Swedish Publication Index (Mälardalens högskola) https://gup.ub.gu.se/publication/307872$$DView record from Swedish Publication Index (Göteborgs universitet) https://research.chalmers.se/publication/525856$$DView record from Swedish Publication Index (Chalmers tekniska högskola) |
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| SubjectTerms | Accuracy Amputation Built-in gains & losses Business metrics Chemistry Classification Control algorithms Discriminant analysis Electromyography Engineering Hand movement Instruments & Instrumentation Medical Engineering Medicinteknik Orthopaedics Ortopedi Pattern recognition Prostheses Real‐time signals Software Support vector machines Usability |
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