Knowledge distillation for sEMG-based gesture recognition: enhancing wearable HMI systems with lightweight models.

Qiu F, Dai C, Liu X, Ye X

DOI: 10.1088/1741-2552/ae93f4
Published: 2026 Aug 25
Container: Journal of neural engineering
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1088/1741-2552/ae93f4,
  title = {Knowledge distillation for sEMG-based gesture recognition: enhancing wearable HMI systems with lightweight models.},
  author = {Qiu F and Dai C and Liu X and Ye X},
  year = {2026},
  journal = {Journal of neural engineering},
  doi = {10.1088/1741-2552/ae93f4},
  url = {https://doi.org/10.1088/1741-2552/ae93f4}
}

RIS

TY  - JOUR
TI  - Knowledge distillation for sEMG-based gesture recognition: enhancing wearable HMI systems with lightweight models.
AU  - Qiu F
AU  - Dai C
AU  - Liu X
AU  - Ye X
PY  - 2026
JO  - Journal of neural engineering
DO  - 10.1088/1741-2552/ae93f4
UR  - https://doi.org/10.1088/1741-2552/ae93f4
ER  -

APA

F, Q., C, D., X, L., & X, Y. (2026). Knowledge distillation for sEMG-based gesture recognition: enhancing wearable HMI systems with lightweight models.. Journal of neural engineering. https://doi.org/10.1088/1741-2552/ae93f4

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pubmed · retrieved 2026-09-25T10:44:14.184Z