The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
EMG feature evaluation for movement control of upper extremity prostheses
Abstract
A variety of EMG features have been evaluated for control of myoelectric upper extremity prostheses. Movement class discrimination, robustness, and computational complexity of these features have been investigated for different time window sizes and noise levels. The measurements include novel application of the Davies-Bouldin index, a measure of cluster separability, and the K-nearest neighbor nonparametric classifier. The features evaluated are the integral of average value, the variance, the number of zero crossings, the Willison amplitude, the v-order and log detectors, and autoregressive model parameters. A new feature, the EMG Histogram, is introduced and shown to be the most effective of the group. The experiments were done on the data acquired from the residual biceps and triceps muscle of an above-elbow amputee.
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“Several features can be extracted in both time and frequency domains (Zardoshti-Kermani et al., 1995; Englehart, 1998; Pattichis and Pattichis, 1999; Sparto et al., 2000) that increase the possibility of controlling more than two degrees of freedom of the prosthesis when used in combination with advanced classification algorithms (Kelly et al., 1990; Christodoulou and Pattichis, 1999; Micera et al”
From A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems (2005) · cites this paper as Zardoshti-Kermani et al., 1995
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Cites
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A new strategy for multifunction myoelectric control
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ON AUTOMATIC FEATURE SELECTION
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Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes
Graupe, D., & Cline, W. K. (1975) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1975.5408479 · Cited by 354
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EMG Pattern Analysis and Classification for a Prosthetic Arm
Saridis, G. N., & Gootee, T. P. (1982) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1982.324954 · Cited by 238
Flagged first, then by citations
Research works printed but not linked: 4
- zardoshti-kermani, 1990, A hybrid method for processing electromyogram information using both neural network and rule-based approaches, Proc Signal Processing Conf (ISSPA 90)
- willison, 1963, A method for measuring motor unit activity in human muscle, J Physiol, 168, 35
- zardoshti-kermani, 1992, Finding the number of neurons in extracellular recordings: A comparison of the effectiveness of popular clustering indices, Proc Comput Neurosci Symp (CNS '92), 229
- zardoshti-kermani, 1989, An intelligent strategy for motion interpretation in an EMG-controlled prosthesis: An application of AI to biomedical engineering, Computational Intell II Proc Int Symp Computational Intell '89
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Feature reduction and selection for EMG signal classification
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.102 · Cited by 1,605
Surface Electromyography Signal Processing and Classification Techniques
Chowdhury, R. H., Reaz, M. B. I., Ali, M. H., Bakar, A. A. A., Chellappan, K., & Chang, T. (2013) · Sensors · doi:10.3390/s130912431 · Cited by 1,013
Electromyography data for non-invasive naturally-controlled robotic hand prostheses
Atzori, M., Gijsberts, A., Castellini, C., Barbara Caputo, B., Hager, A.-G. M., Elsig, S., Giatsidis, G., Bassetto, F., & Müller, H. (2014) · Scientific Data · doi:10.1038/sdata.2014.53 · Cited by 1,023
Scheme, E., & Englehart, K. B. (2011) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2010.09.0177 · Cited by 1,006
Navarro, X., Krueger, T. B., Lago, N., Micera, S., Stieglitz, T., & Dario, P. (2005) · Journal of the Peripheral Nervous System · doi:10.1111/j.1085-9489.2005.10303.x · Cited by 845
Metadata
- Authors
- Zardoshti-Kermani, M., Wheeler, B. C., Badie, K., & Hashemi, R. M.
- Affiliations
- Amirkabir University of Technology (IR)
- University of Illinois Urbana-Champaign (US)
- ICT Research Institute (IR)
- Venue
- IEEE Transactions on Rehabilitation Engineering
- Published
- 1995
- Volume
- 3
- Issue
- 4
- Pages
- 324-333
- ISSN
- 1063-6528 (ISSN-L)
- OpenAlex
- W2120422653
- Cited by
- 477
- References
- 25
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 15 · 2023: 22 · 2022: 28 · 2021: 30 · 2020: 33 · 2019: 38 · 2018: 33 · 2017: 33 · 2016: 32 · 2015: 19 · 2014: 27 · 2013: 20 · 2012: 16
- FWCI
- 1.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Rehabilitation Engineering (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- motor control, Davies-Bouldin index, k-nearest neighbors, zero crossings, above-elbow amputation, biceps and triceps, computational complexity
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