Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2021

Abstract

Fine-grained, unobtrusive monitoring of gym exercises can help users track their own exercise routines and also provide corrective feedback. We propose W8-Scope, a system that uses a simple magnetic-cum-accelerometer sensor, mounted on the weight stack of gym exercise machines, to infer various attributes of gym exercise behavior. More specifically, using multiple machine learning models, W8-Scope helps identify who is exercising, what exercise she is doing, how much weight she is lifting, and whether she is committing any common mistakes. Real world studies, conducted with 50 subjects performing 14 different exercises over 103 distinct sessions in two gyms, show that W8-Scope can, at the granularity of individual exercise sets, achieve high accuracy—e.g., identify the weight used with an accuracy of 97.5%, detect commonplace mistakes with 96.7% accuracy and identify the user with 98.7% accuracy. By incorporating an additional, simple IR sensor on the weight stack, the exercise classification accuracy (across the 14 exercises) further increases from 96.93% to 97.51%. Moreover, by adopting incremental learning techniques, W8-Scope can also accurately track these various facets of exercise over longitudinal periods, in spite of the inherent natural changes in a user’s exercising behavior. Our comprehensive analysis also reveals open challenges, such as adapting to the expertise level of individuals or providing in-situ, early feedback, that remain to be addressed.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Pervasive and Mobile Computing

Volume

75

First Page

1

Last Page

20

ISSN

1574-1192

Identifier

10.1016/j.pmcj.2021.101418

Publisher

Elsevier

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1016/j.pmcj.2021.101418

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