TY - GEN
T1 - Using iOS for inconspicuous data collection
T2 - 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2020
AU - Nishiyama, Yuuki
AU - Ferreira, Denzil
AU - Sasaki, Wataru
AU - Okoshi, Tadashi
AU - Nakazawa, Jin
AU - Dey, Anind K.
AU - Sezaki, Kaoru
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/9/10
Y1 - 2020/9/10
N2 - 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.
AB - 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.
KW - effective data collection
KW - iOS
KW - mobile crowd sensing
KW - mobile sensing toolkit
KW - real-world assessment
UR - http://www.scopus.com/inward/record.url?scp=85091881851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091881851&partnerID=8YFLogxK
U2 - 10.1145/3410530.3414369
DO - 10.1145/3410530.3414369
M3 - Conference contribution
AN - SCOPUS:85091881851
T3 - UbiComp/ISWC 2020 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
SP - 261
EP - 266
BT - UbiComp/ISWC 2020 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
PB - Association for Computing Machinery
Y2 - 12 September 2020 through 17 September 2020
ER -