Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions
Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we...
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| creator | Waris, Asim Niazi, Imran K. Jamil, Mohsin Englehart, Kevin Jensen, Winnie Kamavuako, Ernest Nlandu |
| description | Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P |
| doi_str_mv | 10.1109/JBHI.2018.2864335 |
| format | Article |
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Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P <; 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P <; 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. Furthermore, training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.</description><identifier>ISSN: 2168-2194</identifier><identifier>EISSN: 2168-2208</identifier><identifier>DOI: 10.1109/JBHI.2018.2864335</identifier><identifier>PMID: 30106701</identifier><identifier>CODEN: IJBHA9</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Adolescent ; Adult ; Algorithms ; Artificial Limbs ; Artificial neural networks ; Classification ; Classifiers ; Comparative analysis ; Decision analysis ; Decision trees ; Discriminant analysis ; Electrodes ; Electromyography ; Electromyography - classification ; Electromyography - methods ; Hand - physiology ; Humans ; intra-muscular ; Male ; Middle Aged ; Movement - physiology ; Muscle, Skeletal - physiology ; Muscles ; myoelectric control ; Myoelectricity ; Neural networks ; Neural Networks, Computer ; pattern recognition ; Pattern Recognition, Automated - methods ; Prostheses ; Prosthetics ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Support vector machines ; Surface impedance ; Time dependence ; Variance analysis ; Young Adult</subject><ispartof>IEEE journal of biomedical and health informatics, 2019-07, Vol.23 (4), p.1526-1534</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P <; 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P <; 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. 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Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P <; 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P <; 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. Furthermore, training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>30106701</pmid><doi>10.1109/JBHI.2018.2864335</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-8752-7224</orcidid><orcidid>https://orcid.org/0000-0003-4525-1121</orcidid><orcidid>https://orcid.org/0000-0001-6846-2090</orcidid><orcidid>https://orcid.org/0000-0002-8835-2451</orcidid><orcidid>https://orcid.org/0000-0002-0190-0700</orcidid><orcidid>https://orcid.org/0000-0001-9510-8847</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adolescent Adult Algorithms Artificial Limbs Artificial neural networks Classification Classifiers Comparative analysis Decision analysis Decision trees Discriminant analysis Electrodes Electromyography Electromyography - classification Electromyography - methods Hand - physiology Humans intra-muscular Male Middle Aged Movement - physiology Muscle, Skeletal - physiology Muscles myoelectric control Myoelectricity Neural networks Neural Networks, Computer pattern recognition Pattern Recognition, Automated - methods Prostheses Prosthetics Signal Processing, Computer-Assisted Support Vector Machine Support vector machines Surface impedance Time dependence Variance analysis Young Adult |
| title | Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions |
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