@inproceedings{10.1145/3410530.3414369,
title = {Using IOS for Inconspicuous Data Collection: A Real-World Assessment},
author = {Yuuki Nishiyama and Denzil Ferreira and Wataru Sasaki and Tadashi Okoshi and Jin Nakazawa and Anind K Dey and Kaoru Sezaki},
url = {https://doi.org/10.1145/3410530.3414369
https://www.yuukinishiyama.com/wp-content/uploads/2020/09/AWARE_HASCA2020_UbiComp2020.pdf},
doi = {10.1145/3410530.3414369},
isbn = {9781450380768},
year = {2020},
date = {2020-09-12},
urldate = {2020-09-12},
booktitle = {Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers},
pages = {261–266},
publisher = {Association for Computing Machinery},
address = {Virtual Event, Mexico},
series = {UbiComp-ISWC '20},
abstract = {Mobile Crowd Sensing (MCS) is a method for collecting multiple sensor data from distributed mobile devices for understanding social and behavioral phenomena. The method requires collecting the sensor data 24/7, ideally inconspicuously to minimize bias. Although several MCS tools for collecting the sensor data from an off-the-shelf smartphone are proposed and evaluated under controlled conditions as a benchmark, the performance in a practical sensing study condition is scarce, especially on iOS. In this paper, we assess the data collection quality of AWARE iOS, installed on off-the-shelf iOS smartphones with 9 participants for a week. Our analysis shows that more than 97% of sensor data, provided by hardware sensors (i.e., accelerometer, location, and pedometer sensor), is successfully collected in real-world conditions, unless a user explicitly quits our data collection application.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}