A Lightweight Machine Learning Framework for Post-Stroke Gait Abnormality Classification Using Wearable Gyroscope Features.
Saved in:
| Title: | A Lightweight Machine Learning Framework for Post-Stroke Gait Abnormality Classification Using Wearable Gyroscope Features. |
|---|---|
| Authors: | Orfanos S; Bioassist SA, 26504 Rio, Greece., Sanghan T; Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand., Menychtas A; Department of Digital Systems, Faculty of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, Greece., Panagopoulos C; Bioassist SA, 26504 Rio, Greece., Maglogiannis I; Department of Digital Systems, Faculty of Information and Communication Technologies, University of Piraeus, 18534 Piraeus, Greece., Chatpun S; Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand. |
| Source: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 May 15; Vol. 26 (10). Date of Electronic Publication: 2026 May 15. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 1424-8220 |
|---|---|
| DOI: | 10.3390/s26103143 |
