Yuuki Nishiyama presented his research “Using IOS for Inconspicuous Data Collection: A Real-World Assessment” at an in-conjunction workshop (HASCA2020) in ACM UbiComp2020

Yuuki Nishiyama presented his research “Using IOS for Inconspicuous Data Collection: A Real-World Assessment” at an in-conjunction workshop (HASCA2020) in ACM UbiComp2020.

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. 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

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.

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Yuuki Nishiyama presented his research “Using IOS for Inconspicuous Data Collection: A Real-World Assessment” at an in-conjunction workshop (HASCA2020) in ACM UbiComp2020