Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2020

Abstract

We present ERICA, a digital personal trainer for users performing free weights exercises, with two key differentiators: (a) First, unlike prior approaches that either require multiple on-body wearables or specialized infrastructural sensing, ERICA uses a single in-ear "earable" device (piggybacking on a form factor routinely used by millions of gym-goers) and a simple inertial sensor mounted on each weight equipment; (b) Second, unlike prior work that focuses primarily on quantifying a workout, ERICA additionally identifies a variety of fine-grained exercising mistakes and delivers real-time, in-situ corrective instructions. To achieve this, we (a) design a robust approach for user-equipment association that can handle multiple (even 15) concurrently exercising users; (b) develop a suite of statistical models to detect several commonplace repetition-level mistakes; and (c) experimentally study the efficacy of multiple in-situ corrective feedback strategies. Via an end-to-end evaluation of ERICA with 33 participants naturally performing 3 dumbbell-based exercises, we show that (a) ERICA identifies over 94% of mistakes during the first 5 repetitions of a set, (b) the resulting feedback is viewed favorably by 78% of users, and (c) the feedback is effective, reducing mistakes by 10+% during subsequent repetitions.

Keywords

smart gym, digital personal trainer, earables, free-weights exercises, Internet of things (IoT), personalized feedback

Discipline

Software Engineering | Sports Sciences

Research Areas

Software and Cyber-Physical Systems

Publication

Sensys '20: Proceedings of the 18th ACM Conference on Embedded Networked Sensor Systems, November 16-19, Yokohama, Japan, and Virtual

First Page

558

Last Page

571

ISBN

9781450375900

Identifier

10.1145/3384419.3430732

Publisher

ACM

City or Country

New York

Embargo Period

4-15-2021

Copyright Owner and License

LARC and Authors

Additional URL

https://doi.org/10.1145/3384419.3430732

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