The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait
Abstract
The aim of this work is to present an original double-threshold detector of muscle activation, specifically developed for gait analysis. This detector operates on the raw myoelectric signal and, hence, it does not require any envelope detection. Its performances are fixed by the values of three parameters, namely, false-alarm probability (Pfa), detection probability, and time resolution. Double-threshold detectors are preferable to single-threshold ones because, for a fixed value of the Pfa, they yield higher detection probability; furthermore, they allow the user to select the couple false alarm-detection probability with a higher degree of freedom, thus, adapting the performances of the detector to the characteristics of the myoelectric signal of interest and of the experimental situation. In this paper, first we derive the detection algorithm and describe different strategies for selecting its parameters, then we present the performances of the proposed procedure evaluated by means of computer simulations, and finally we report an example of application to myoelectric signals recorded during gait. The characterization of the proposed double-threshold detector demonstrates that, in most practical situations, the bias of the estimates of the on-off transitions is smaller than 10 ms, the standard deviation may be kept lower than 15 ms, and the percentage of erroneous patterns is below 5%. These results show that this detection approach is satisfactory in research applications as well as in the clinical practice.
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Cited by
Techniques of EMG signal analysis: detection, processing, classification and applications
Reaz, M. B. I., Hussain, M. S., & Mohd-Yasin, F. (2006) · Biological Procedures Online · doi:10.1251/bpo115 · Cited by 1,629
Myoelectric control systems—A survey
Asghari Oskoei, M., & Hu, H. (2007) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.07.009 · Cited by 1,244
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
Gait abnormalities in minimally impaired multiple sclerosis patients
Benedetti, M. G., Piperno, R., Simoncini, L., Bonato, P., Tonini, A., & Giannini, S. (1999) · Multiple Sclerosis Journal · doi:10.1177/135245859900500510 · Cited by 289
Self-citationObjective motor response onset detection in surface myoelectric signals
Staude, G., & Wolf, W. (1999) · Medical Engineering & Physics · doi:10.1016/s1350-4533(99)00067-3 · Cited by 217
Metadata
- Authors
- Bonato, P., D'Alessio, T., & Knaflitz, M.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 1998-03
- Volume
- 45
- Issue
- 3
- Pages
- 287-299
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2136908787
- Cited by
- 375
- References
- 21
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bonato, P., D'Alessio, T., & Knaflitz, M. (1998). A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.661154
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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