Wrist-Worn Wearables Based on Force Myography: On the Significance of User Anthropometry

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Faculty/Staff
Final version published as: 

Delva, M.L., Lajoie, K., Khoshnam, M. et al. Wrist-worn wearables based on force myography: on the significance of user anthropometry. BioMed Eng OnLine 19, 46 (2020). https://doi.org/10.1186/s12938-020-00789-w.

Date created: 
2020-06-12
Identifier: 
DOI: 10.1186/s12938-020-00789-w
Keywords: 
Wearable technology
Force myography
User anthropometry
Biosignal quality
Machine learning
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| < 0.67) and strong (0.67 ≤ |R|) relationships for ratio of skinfold thickness to forearm circumference, grip strength and ratio of wrist to forearm circumference. These anthropometric features contributed to 23–30% of the variability in FMG signal acquisition and as much as 50% of the variability in classification accuracy for single gestures.

Conclusions

Increased grip strength, larger forearm girth, and smaller skinfold-to-forearm circumference ratio improve signal quality and gesture classification accuracy.

Language: 
English
Document type: 
Article
File(s): 
Sponsor(s): 
Natural Sciences and Engineering Research Council of Canada (NSERC)
Canadian Institutes of Health Research (CIHR)
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