Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

3-2020

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 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. 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.

Discipline

Computer Sciences | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2020 IEEE International Conference on Persuasive Computing and Communications (PerCom): March 23-27, Austin, TX: Proceedings

First Page

1

Last Page

10

ISBN

9781728146577

Identifier

10.1109/PerCom45495.2020.9127379

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/PerCom45495.2020.9127379

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