ERICA: enabling real-time mistake detection & corrective feedback for free-weights exercises
- Title
- ERICA: enabling real-time mistake detection & corrective feedback for free-weights exercises
- Authors
- Radhakrishinan, Meera; Rathnayake, Darshana; Ong, Koon Han; HWANG, INSEOK; Misra, Archan
- Date Issued
- 2020-11-19
- Publisher
- ACM
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/104343
- Article Type
- Conference
- Citation
- SenSys 2020 (The 18th ACM Conference on Embedded Networked Sensor Systems), page. 558 - 571, 2020-11-19
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