Gait Analysis of Electromyographic Spectral Differences in Stroke Survivors and Healthy Controls
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62520-6_13
Reference20 articles.
1. Feigin, V.L., et al.: World stroke organization: global stroke fact sheet 2022. Int. J. Stroke 17(1), 18–29 (2022). https://doi.org/10.1177/17474930221080343
2. Yew, K.S., Cheng, E.: Acute stroke diagnosis. Am. Fam. Physician 80(1), 33–40 (2009)
3. Pasmanasari, E.D., Pawitan, J.A.: The potential of electromyography signals as markers to detect and monitor Parkinson’s Disease. Biomed. Pharmacol. J. 14(1), 373–378 (2021). https://doi.org/10.13005/bpj/2136
4. Celik, Y., Stuart, S., Woo, W., Sejdic, E., Godfrey, A.: Multi-modal gait: a wearable, algorithm and data fusion approach for clinical and free-living assessment. Inf. Fusion 78, 57–70 (2022). https://doi.org/10.1016/j.inffus.2021.09.016
5. Go, S.A., Coleman-Wood, K., Kaufman, K.R.: Frequency analysis of lower extremity electromyography signals for the quantitative diagnosis of dystonia. J. Electromyogr. Kinesiol. 24(1), 31–36 (2014). https://doi.org/10.1016/j.jelekin.2013.11.002
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