The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A Comparison of the Effects of Electrode Implantation and Targeting on Pattern Classification Accuracy for Prosthesis Control
Open accessAbstract
The use of surface versus intramuscular electrodes as well as the effect of electrode targeting on pattern-recognition-based multifunctional prosthesis control was explored. Surface electrodes are touted for their ability to record activity from relatively large portions of muscle tissue. Intramuscular electromyograms (EMGs) can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk. However, little work has been done to compare the two. Additionally, while previous investigations have either targeted electrodes to specific muscles or used untargeted (symmetric) electrode arrays, no work has compared these approaches to determine if one is superior. The classification accuracies of pattern-recognition-based classifiers utilizing surface and intramuscular as well as targeted and untargeted electrodes were compared across 11 subjects. A repeated-measures analysis of variance revealed that when only EMG amplitude information was used from all available EMG channels, the targeted surface, targeted intramuscular, and untargeted surface electrodes produced similar classification accuracies while the untargeted intramuscular electrodes produced significantly lower accuracies. However, no statistical differences were observed between any of the electrode conditions when additional features were extracted from the EMG signal. It was concluded that the choice of electrode should be driven by clinical factors, such as signal robustness/stability, cost, etc., instead of by classification accuracy.
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Cites
A robust, real-time control scheme for multifunction myoelectric control
Englehart, K., & Hudgins, B. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.813539 · Cited by 1,743
Book Review: Discovering Statistics Using SPSS
Field, A., & Gaitanou, C. (2005) · PsyPag Quarterly · doi:10.53841/bpspag.2005.1.56.31 · Cited by 679
Graupe, D., Salahi, J., & Kohn, K. H. (1982) · Journal of Biomedical Engineering · doi:10.1016/0141-5425(82)90021-8 · Cited by 205
Micera, S., Sabatini, A. M., Dario, P., & Rossi, B. (1999) · Medical Engineering & Physics · doi:10.1016/s1350-4533(99)00055-7 · Cited by 90
Cited by
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
Classification of Simultaneous Movements Using Surface EMG Pattern Recognition
Young, A. J., Smith, L. H., Rouse, E. J., & Hargrove, L. J. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2232293 · Cited by 320
Young, A. J., Hargrove, L. J., & Kuiken, T. A. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2159216 · Cited by 287
A survey of sensor fusion methods in wearable robotics
Novak, D., & Riener, R. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.08.012 · Cited by 252
Samuel, O. W., Asogbon, M. G., Geng, Y., Al-Timemy, A. H., Pirbhulal, S., Ji, N., Chen, S., Fang, P., & Li, G. (2019) · IEEE Access · doi:10.1109/access.2019.2891350 · Cited by 185
Metadata
- Authors
- Farrell, T. R., & Weir, R. F. F.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2008-09
- Volume
- 55
- Issue
- 9
- Pages
- 2198-2211
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2153043277
- Cited by
- 159
- References
- 4
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- green
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