Reducing the number of EMG electrodes during online hand gesture classification with changing wrist positions
Abstract Background Myoelectric control based on hand gesture classification can be used for effective, contactless human–machine interfacing in general applications (e.g., consumer market) as well as in the clinical context. However, the accuracy of hand gesture classification can be impacted by se...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12984-022-01056-w |
| _version_ | 1818509980543746048 |
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| author | Luis Pelaez Murciego Mauricio C. Henrich Erika G. Spaich Strahinja Dosen |
| author_facet | Luis Pelaez Murciego Mauricio C. Henrich Erika G. Spaich Strahinja Dosen |
| author_sort | Luis Pelaez Murciego |
| collection | DOAJ |
| description | Abstract Background Myoelectric control based on hand gesture classification can be used for effective, contactless human–machine interfacing in general applications (e.g., consumer market) as well as in the clinical context. However, the accuracy of hand gesture classification can be impacted by several factors including changing wrist position. The present study aimed at investigating how channel configuration (number and placement of electrode pads) affects performance in hand gesture recognition across wrist positions, with the overall goal of reducing the number of channels without the loss of performance with respect to the benchmark (all channels). Methods Matrix electrodes (256 channels) were used to record high-density EMG from the forearm of 13 healthy subjects performing a set of 8 gestures in 3 wrist positions and 2 force levels (low and moderate). A reduced set of channels was chosen by applying sequential forward selection (SFS) and simple circumferential placement (CIRC) and used for gesture classification with linear discriminant analysis. The classification success rate and task completion rate were the main outcome measures for offline analysis across the different number of channels and online control using 8 selected channels, respectively. Results The offline analysis demonstrated that good accuracy (> 90%) can be achieved with only a few channels. However, using data from all wrist positions required more channels to reach the same performance. Despite the targeted placement (SFS) performing similarly to CIRC in the offline analysis, the task completion rate [median (lower–upper quartile)] in the online control was significantly higher for SFS [71.4% (64.8–76.2%)] compared to CIRC [57.1% (51.8–64.8%), p < 0.01], especially for low contraction levels [76.2% (66.7–84.5%) for SFS vs. 57.1% (47.6–60.7%) for CIRC, p < 0.01]. For the reduced number of electrodes, the performance with SFS was comparable to that obtained when using the full matrix, while the selected electrodes were highly subject-specific. Conclusions The present study demonstrated that the number of channels required for gesture classification with changing wrist positions could be decreased substantially without loss of performance, if those channels are placed strategically along the forearm and individually for each subject. The results also emphasize the importance of online assessment and motivate the development of configurable matrix electrodes with integrated channel selection. |
| first_indexed | 2024-12-10T22:52:54Z |
| format | Article |
| id | doaj.art-c31ab6e7dbc14ca594fc7d1ecdb17fd3 |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-10T22:52:54Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-c31ab6e7dbc14ca594fc7d1ecdb17fd32022-12-22T01:30:22ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032022-07-0119111610.1186/s12984-022-01056-wReducing the number of EMG electrodes during online hand gesture classification with changing wrist positionsLuis Pelaez Murciego0Mauricio C. Henrich1Erika G. Spaich2Strahinja Dosen3Neurorehabilitation Systems, Department of Health Science and Technology, Aalborg UniversityNeurorehabilitation Systems, Department of Health Science and Technology, Aalborg UniversityNeurorehabilitation Systems, Department of Health Science and Technology, Aalborg UniversityNeurorehabilitation Systems, Department of Health Science and Technology, Aalborg UniversityAbstract Background Myoelectric control based on hand gesture classification can be used for effective, contactless human–machine interfacing in general applications (e.g., consumer market) as well as in the clinical context. However, the accuracy of hand gesture classification can be impacted by several factors including changing wrist position. The present study aimed at investigating how channel configuration (number and placement of electrode pads) affects performance in hand gesture recognition across wrist positions, with the overall goal of reducing the number of channels without the loss of performance with respect to the benchmark (all channels). Methods Matrix electrodes (256 channels) were used to record high-density EMG from the forearm of 13 healthy subjects performing a set of 8 gestures in 3 wrist positions and 2 force levels (low and moderate). A reduced set of channels was chosen by applying sequential forward selection (SFS) and simple circumferential placement (CIRC) and used for gesture classification with linear discriminant analysis. The classification success rate and task completion rate were the main outcome measures for offline analysis across the different number of channels and online control using 8 selected channels, respectively. Results The offline analysis demonstrated that good accuracy (> 90%) can be achieved with only a few channels. However, using data from all wrist positions required more channels to reach the same performance. Despite the targeted placement (SFS) performing similarly to CIRC in the offline analysis, the task completion rate [median (lower–upper quartile)] in the online control was significantly higher for SFS [71.4% (64.8–76.2%)] compared to CIRC [57.1% (51.8–64.8%), p < 0.01], especially for low contraction levels [76.2% (66.7–84.5%) for SFS vs. 57.1% (47.6–60.7%) for CIRC, p < 0.01]. For the reduced number of electrodes, the performance with SFS was comparable to that obtained when using the full matrix, while the selected electrodes were highly subject-specific. Conclusions The present study demonstrated that the number of channels required for gesture classification with changing wrist positions could be decreased substantially without loss of performance, if those channels are placed strategically along the forearm and individually for each subject. The results also emphasize the importance of online assessment and motivate the development of configurable matrix electrodes with integrated channel selection.https://doi.org/10.1186/s12984-022-01056-wSurface electromyographyHigh-density electrodesHuman–machine interfacesGesture recognitionHand rehabilitationRehabilitation robotics |
| spellingShingle | Luis Pelaez Murciego Mauricio C. Henrich Erika G. Spaich Strahinja Dosen Reducing the number of EMG electrodes during online hand gesture classification with changing wrist positions Journal of NeuroEngineering and Rehabilitation Surface electromyography High-density electrodes Human–machine interfaces Gesture recognition Hand rehabilitation Rehabilitation robotics |
| title | Reducing the number of EMG electrodes during online hand gesture classification with changing wrist positions |
| title_full | Reducing the number of EMG electrodes during online hand gesture classification with changing wrist positions |
| title_fullStr | Reducing the number of EMG electrodes during online hand gesture classification with changing wrist positions |
| title_full_unstemmed | Reducing the number of EMG electrodes during online hand gesture classification with changing wrist positions |
| title_short | Reducing the number of EMG electrodes during online hand gesture classification with changing wrist positions |
| title_sort | reducing the number of emg electrodes during online hand gesture classification with changing wrist positions |
| topic | Surface electromyography High-density electrodes Human–machine interfaces Gesture recognition Hand rehabilitation Rehabilitation robotics |
| url | https://doi.org/10.1186/s12984-022-01056-w |
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