research-article

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

Authors:

Yuuki Nishiyama,

Denzil Ferreira,

Tadashi Okoshi,

Kaoru SezakiAuthors Info & Claims

UbiComp/ISWC '20 Adjunct: 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

https://doi.org/10.1145/3410530.3414369

Published: 12 September 2020 Publication History

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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Cited By

Halabi RSelvarajan RLin ZHerd CLi XKabrit JTummalacherla MChaibub Neto EPratap A(2024)Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World SettingsSensors10.3390/s2419624624:19(6246)Online publication date: 26-Sep-2024
https://doi.org/10.3390/s24196246
Ito MTsubouchi KNishio NShimosaka MTaya ASezaki KNishiyama YKostakos VKay JHoang T(2024)Investigating Acceptable Voice-based Notification Timings through Earable Devices: A Preliminary Field StudyCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3677579(30-34)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3677579
Slade CBenzo RWashington P(2023)Design Guidelines for Improving Mobile Sensing Data Collection: A Prospective Mixed-Methods Study (Preprint)Journal of Medical Internet Research10.2196/55694Online publication date: 20-Dec-2023
https://doi.org/10.2196/55694
Show More Cited By

Index Terms

Using iOS for inconspicuous data collection: a real-world assessment
1. Human-centered computing
  1. Ubiquitous and mobile computing

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Published In

cover image ACM Conferences

UbiComp/ISWC '20 Adjunct: 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

September 2020

732 pages

ISBN:9781450380768

DOI:10.1145/3410530

General Chairs:
Monica Tentori
CICESE, Mexico
,
Nadir Weibel
UC San Diego
,
Kristof Van Laerhoven
University of Siegen, Germany
,
Program Chairs:
Gregory Abowd
Georgia Tech
,
Flora Salim
RMIT, Australia

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 12 September 2020

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UbiComp/ISWC '20

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UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers

September 12 - 17, 2020

Virtual Event, Mexico

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Halabi RSelvarajan RLin ZHerd CLi XKabrit JTummalacherla MChaibub Neto EPratap A(2024)Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World SettingsSensors10.3390/s2419624624:19(6246)Online publication date: 26-Sep-2024
https://doi.org/10.3390/s24196246
Ito MTsubouchi KNishio NShimosaka MTaya ASezaki KNishiyama YKostakos VKay JHoang T(2024)Investigating Acceptable Voice-based Notification Timings through Earable Devices: A Preliminary Field StudyCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3677579(30-34)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3677579
Slade CBenzo RWashington P(2023)Design Guidelines for Improving Mobile Sensing Data Collection: A Prospective Mixed-Methods Study (Preprint)Journal of Medical Internet Research10.2196/55694Online publication date: 20-Dec-2023
https://doi.org/10.2196/55694
Li SHalabi RSelvarajan RWoerner MFillipo IBanerjee SMosser BJain FAreán PPratap A(2022)Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world StudyJMIR Formative Research10.2196/407656:11(e40765)Online publication date: 14-Nov-2022
https://doi.org/10.2196/40765
Emden DGoltermann JDannlowski UHahn TOpel N(2021)Technical feasibility and adherence of the Remote Monitoring Application in Psychiatry (ReMAP) for the assessment of affective symptomsJournal of Affective Disorders10.1016/j.jad.2021.07.030294(652-660)Online publication date: Nov-2021
https://doi.org/10.1016/j.jad.2021.07.030

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