Wrist-worn wearables based on force myography: on the significance of user anthropometry
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BioMedical Engineering OnLine Open Access
RESEARCH
Wrist‑worn wearables based on force myography: on the significance of user anthropometry Mona Lisa Delva, Kim Lajoie , Mahta Khoshnam and Carlo Menon* *Correspondence: [email protected] Menrva Research Group, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Metro Vancouver, Unit 250, 13450 102nd Avenue, Surrey, BC V5A 1S6, Canada
Abstract Background: Force myography (FMG) is a non-invasive technology used to track functional movements and hand gestures by sensing volumetric changes in the limbs caused by muscle contraction. Force transmission through tissue implies that differences in tissue mechanics and/or architecture might impact FMG signal acquisition and the accuracy of gesture classifier models. The aim of this study is to identify if and how user anthropometry affects the quality of FMG signal acquisition and the performance of machine learning models trained to classify different hand and wrist gestures based on that data. Methods: Wrist and forearm anthropometric measures were collected from a total of 21 volunteers aged between 22 and 82 years old. Participants performed a set of tasks while wearing a custom-designed FMG band. Primary outcome measure was the Spearman’s correlation coefficient (R) between the anthropometric measures and FMG signal quality/ML model performance. Results: Results demonstrated moderate (0.3 ≤|R|
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