University of Oulu

Yuuki Nishiyama, Denzil Ferreira, Wataru Sasaki, Tadashi Okoshi, Jin Nakazawa, Anind K. Dey, and Kaoru Sezaki. 2020. 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 (UbiComp-ISWC '20). Association for Computing Machinery, New York, NY, USA, 261–266. DOI:https://doi.org/10.1145/3410530.3414369

Using iOS for inconspicuous data collection : a real-world assessment

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Author: Nishiyama, Yuuki1; Ferreira, Denzil2; Sasaki, Wataru3;
Organizations: 1The University of Tokyo, Japan
2University of Oulu, Finland
3Keio University Japan
4University of Washington, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020110989694
Language: English
Published: Association for Computing Machinery, 2020
Publish Date: 2020-11-09
Description:

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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ISBN Print: 978-1-4503-8076-8
Pages: 261 - 266
DOI: 10.1145/3410530.3414369
OADOI: https://oadoi.org/10.1145/3410530.3414369
Host publication: UbiComp-ISWC '20: 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
Conference: ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
iOS
Funding: Thiswork was supported by JSPS KAKENHI Grant Number JP18K11274, JP20H00622, JP20K19840 and by Academy of Finland 316253-320089 SENSATE, 318927 6Genesis Flagship.
Academy of Finland Grant Number: 318927
316253
320089
Detailed Information: 318927 (Academy of Finland Funding decision)
316253 (Academy of Finland Funding decision)
320089 (Academy of Finland Funding decision)
Copyright information: © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UbiComp-ISWC '20: 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, https://doi.org/10.1145/3410530.3414369.