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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Features extracted in time and frequency domains increase the possibility of controlling more than two degrees of freedom when combined with advanced classification algorithms and on-line learning.
“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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Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition
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- 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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