DC Field | Value | Language |
---|---|---|
dc.contributor.author | Radhakrishinan, Meera | - |
dc.contributor.author | Rathnayake, Darshana | - |
dc.contributor.author | Ong, Koon Han | - |
dc.contributor.author | HWANG, INSEOK | - |
dc.contributor.author | Misra, Archan | - |
dc.date.accessioned | 2020-11-25T04:50:10Z | - |
dc.date.available | 2020-11-25T04:50:10Z | - |
dc.date.created | 2020-11-25 | - |
dc.date.issued | 2020-11-19 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/104343 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | ACM | - |
dc.relation.isPartOf | SenSys 2020 (The 18th ACM Conference on Embedded Networked Sensor Systems) | - |
dc.relation.isPartOf | Proceedings of The 18th ACM Conference on Embedded Networked Sensor Systems | - |
dc.title | ERICA: enabling real-time mistake detection & corrective feedback for free-weights exercises | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | SenSys 2020 (The 18th ACM Conference on Embedded Networked Sensor Systems), pp.558 - 571 | - |
dc.citation.conferenceDate | 2020-11-16 | - |
dc.citation.conferencePlace | JA | - |
dc.citation.endPage | 571 | - |
dc.citation.startPage | 558 | - |
dc.citation.title | SenSys 2020 (The 18th ACM Conference on Embedded Networked Sensor Systems) | - |
dc.contributor.affiliatedAuthor | HWANG, INSEOK | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
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