The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Decoding of Individuated Finger Movements Using Surface Electromyography
Abstract
Upper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals. While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movements would require more elaborate control schemes. We show that it is possible to decode individual flexion and extension movements of each finger (ten movements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference ( p < 0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.
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Citations
Cites
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Cited by
Feature reduction and selection for EMG signal classification
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“The sEMG electrodes are positioned in order to combine two methods that are common in the field, i.e., a dense sampling approach (Fukuda et al., 2003; Tenore et al., 2009; Li et al., 2010) and a precise anatomical positioning strategy (De Luca, 1997; Castellini et al., 2009).”
BackgroundMethodResultInfluentialMyoelectric forearm prostheses: State of the art from a user-centered perspective
Peerdeman, B., Boere, D., Witteveen, H., Huis in `tVeld, R., Hermens, H., Stramigioli, S., Rietman, H., Veltink, P., & Misra, S. (2011) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2010.08.0161 · Cited by 469
Control of Hand Prostheses Using Peripheral Information
Micera, S., Carpaneto, J., & Raspopovic, S. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2085429 · Cited by 403
Wu, H., Yang, G., Zhu, K., Liu, S., Guo, W., Jiang, Z., & Li, Z. (2021) · Advanced Science · doi:10.1002/advs.202001938 · Cited by 395
Metadata
- Authors
- Tenore, F. V. G., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, R., & Thakor, N. V.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2009-05
- Volume
- 56
- Issue
- 5
- Pages
- 1427-1434
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2113190957
- Cited by
- 342
- References
- 21
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- closed
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