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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Kapelner, Tamás Vujaklija, Ivan Jiang, Ning Negro, Francesco Aszmann, Oskar C. Principe, Jose Farina, Dario |
| Abstract | Background Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. Methods We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. Results The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). Conclusions These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control. |
| Related Links | https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-019-0516-x.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17430003 |
| DOI | 10.1186/s12984-019-0516-x |
| Journal | Journal of NeuroEngineering and Rehabilitation |
| Issue Number | 1 |
| Volume Number | 16 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2019-04-05 |
| Access Restriction | Open |
| Subject Keyword | Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering Prosthesis control EMG decomposition Neural information Motor units |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Rehabilitation |
| Journal Impact Factor | 5.2/2023 |
| 5-Year Journal Impact Factor | 5.6/2023 |
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