助教の西山がHASCA2020（ACM UbiComp2020 併設ワークショップ）にて研究発表「Using IOS for Inconspicuous Data Collection: A Real-World Assessment」を行いました！

助教の西山が、ACM UbiComp2020の併設ワークショップHASCA2020にて研究発表を行ました。

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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助教の西山がHASCA2020（ACM UbiComp2020 併設ワークショップ）にて研究発表「Using IOS for Inconspicuous Data Collection: A Real-World Assessment」を行いました！