A multiclassifier system with dynamic ensemble selection applied to the recognition of EMG signals for the control of bio-prosthetic hand
The paper presents a concept of hand movements recognition on the basis of EMG signal analysis. Signal features are represented by coefficient of autoregressive (AR) model, and as classifier the original multiclassifier systems with dynamic ensemble selection are applied. The performance of the prop...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/28489 |
| _version_ | 1848752550885457920 |
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| author | Kurzynski, M. Woloszynski, Tomasz Wolczowski, A. |
| author_facet | Kurzynski, M. Woloszynski, Tomasz Wolczowski, A. |
| author_sort | Kurzynski, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The paper presents a concept of hand movements recognition on the basis of EMG signal analysis. Signal features are represented by coefficient of autoregressive (AR) model, and as classifier the original multiclassifier systems with dynamic ensemble selection are applied. The performance of the proposed methods was experimentally compared against three classifiers using real datasets. The systems developed achieved the highest overall classification accuracies demonstrating the potential of dynamic classifier selection for recognition of EMG signals. ©2010 IEEE. |
| first_indexed | 2025-11-14T08:10:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-28489 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:10:25Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-284892017-09-13T15:20:01Z A multiclassifier system with dynamic ensemble selection applied to the recognition of EMG signals for the control of bio-prosthetic hand Kurzynski, M. Woloszynski, Tomasz Wolczowski, A. The paper presents a concept of hand movements recognition on the basis of EMG signal analysis. Signal features are represented by coefficient of autoregressive (AR) model, and as classifier the original multiclassifier systems with dynamic ensemble selection are applied. The performance of the proposed methods was experimentally compared against three classifiers using real datasets. The systems developed achieved the highest overall classification accuracies demonstrating the potential of dynamic classifier selection for recognition of EMG signals. ©2010 IEEE. 2010 Conference Paper http://hdl.handle.net/20.500.11937/28489 10.1109/ISABEL.2010.5702931 restricted |
| spellingShingle | Kurzynski, M. Woloszynski, Tomasz Wolczowski, A. A multiclassifier system with dynamic ensemble selection applied to the recognition of EMG signals for the control of bio-prosthetic hand |
| title | A multiclassifier system with dynamic ensemble selection applied to the recognition of EMG signals for the control of bio-prosthetic hand |
| title_full | A multiclassifier system with dynamic ensemble selection applied to the recognition of EMG signals for the control of bio-prosthetic hand |
| title_fullStr | A multiclassifier system with dynamic ensemble selection applied to the recognition of EMG signals for the control of bio-prosthetic hand |
| title_full_unstemmed | A multiclassifier system with dynamic ensemble selection applied to the recognition of EMG signals for the control of bio-prosthetic hand |
| title_short | A multiclassifier system with dynamic ensemble selection applied to the recognition of EMG signals for the control of bio-prosthetic hand |
| title_sort | multiclassifier system with dynamic ensemble selection applied to the recognition of emg signals for the control of bio-prosthetic hand |
| url | http://hdl.handle.net/20.500.11937/28489 |