@inproceedings{10.1145/3460418.3479283,
title = {Experience Sampling Tool for Repetitive Skills Training in Sports Using Voice User Interface},
author = {Yuuki Nishiyama and Kaoru Sezaki},
url = {https://doi.org/10.1145/3460418.3479283},
doi = {10.1145/3460418.3479283},
isbn = {9781450384612},
year = {2021},
date = {2021-01-01},
booktitle = {Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},
pages = {54–55},
publisher = {Association for Computing Machinery},
address = {Virtual, USA},
series = {UbiComp '21},
abstract = {Repetitive skills training (RST) is a commonly used method for improving skills.
Although wearable devices and existing context-aware technologies allow us to easily
detect objective data during RST, subjective data have not been collected effectively,
even though both objective and subjective data are important for RST. In this paper,
we propose and implement a prototype system, called MiQ, to collect subjective data
with minimum workload during RST for sports. MiQ allows us to record subjective data
hands-free via a voice user interface (VUI). We also discuss the future scope of the
proposed prototype system.},
keywords = {context-awareness, Experience Sampling, Voice User Interface, Wearable Devices},
pubstate = {published},
tppubtype = {inproceedings}
}