Sezaki & Nishiyama Laboratory
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Yuuki Nishiyama, Denzil Ferreira, Wataru Sasaki, Tadashi Okoshi, Jin Nakazawa, Anind K Dey, Kaoru Sezaki
Using IOS for Inconspicuous Data Collection: A Real-World Assessment Inproceedings
In: 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, pp. 261–266, Association for Computing Machinery, Virtual Event, Mexico, 2020, ISBN: 9781450380768.
Abstract | BibTeX | タグ: Effective Data Collection, iOS, Mobile Crowd sensing, Mobile Sensing Toolkit, Real-world Assessment | Links:
@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://github.com/tetujin/AWAREFramework-iOS},
doi = {10.1145/3410530.3414369},
isbn = {9781450380768},
year = {2020},
date = {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 = {Effective Data Collection, iOS, Mobile Crowd sensing, Mobile Sensing Toolkit, Real-world Assessment},
pubstate = {published},
tppubtype = {inproceedings}
}