Can Earables Support Effective User Engagement during Weight-Based Gym Exercises?
Proceedings of the 1st International Workshop on Earable Computing, EarComp 2019, Page: 42-47
2019
- 11Citations
- 212Usage
- 13Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations11
- Citation Indexes11
- 11
- CrossRef9
- Usage212
- Downloads170
- Abstract Views42
- Captures13
- Readers13
- 13
Conference Paper Description
We explore the use of personal 'earable' devices (widely used by gym-goers) in providing personalized, quantified insights and feedback to users performing gym exercises. As in-ear sensing by itself is often too weak to pick up exercise-driven motion dynamics, we propose a novel, low-cost system that can monitor multiple concurrent users by fusing data from (a) wireless earphones, equipped with inertial and physiological sensors and (b) inertial sensors attached to exercise equipment. We share preliminary findings from a small-scale study to demonstrate the promise of this approach, as well as identify open challenges.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85081052736&origin=inward; http://dx.doi.org/10.1145/3345615.3361132; https://dl.acm.org/doi/10.1145/3345615.3361132; https://ink.library.smu.edu.sg/sis_research/4758; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5761&context=sis_research; https://dx.doi.org/10.1145/3345615.3361132
Association for Computing Machinery (ACM)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know