More Web Proxy on the site http://driver.im/

research-article

Open access

Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies

Authors:

Laura E. Barnes,

Matthew S. GerberAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 415 - 426

https://doi.org/10.1145/2971648.2971711

Published: 12 September 2016 Publication History

Abstract

The burden of entry into mobile crowdsensing (MCS) is prohibitively high for human-subject researchers who lack a technical orientation. As a result, the benefits of MCS remain beyond the reach of research communities (e.g., psychologists) whose expertise in the study of human behavior might advance applications and understanding of MCS systems. This paper presents Sensus, a new MCS system for human-subject studies that bridges the gap between human-subject researchers and MCS methods. Sensus alleviates technical burdens with on-device, GUI-based design of sensing plans, simple and efficient distribution of sensing plans to study participants, and uniform participant experience across iOS and Android devices. Sensing plans support many hardware and software sensors, automatic deployment of sensor-triggered surveys, and double-blind assignment of participants within randomized controlled trials. Sensus offers these features to study designers without requiring knowledge of markup and programming languages. We demonstrate the feasibility of using Sensus within two human-subject studies, one in psychology and one in engineering. Feedback from non-technical users indicates that Sensus is an effective and low-burden system for MCS-based data collection and analysis.

References

[1]

Nadav Aharony, Wei Pan, Cory Ip, Inas Khayal, and Alex Pentland. 2011. Social fMRI: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing 7, 6 (2011), 643--659.

Digital Library

[2]

American College Health Association and others. 2013. National College Health Assessment II: Reference Group Executive Summary Fall 2012. Hanover, MD: American College Health Association (2013).

[3]

Andrew T Campbell, Shane B Eisenman, Nicholas D Lane, Emiliano Miluzzo, Ronald A Peterson, Hong Lu, Xiao Zheng, Mirco Musolesi, Kristóf Fodor, and Gahng-Seop Ahn. 2008. The rise of people-centric sensing. Internet Computing, IEEE 12, 4 (2008), 12--21.

Digital Library

[4]

Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1293--1304.

Digital Library

[5]

Giuseppe Cardone, Luca Foschini, Paolo Bellavista, Antonio Corradi, Cristian Borcea, Manoop Talasila, and Reza Curtmola. 2013. Fostering participaction in smart cities: a geo-social crowdsensing platform. Communications Magazine, IEEE 51, 6 (2013), 112--119.

[6]

Yohan Chon, Nicholas D. Lane, Fan Li, Hojung Cha, and Feng Zhao. 2012. Automatically Characterizing Places with Opportunistic CrowdSensing using Smartphones. In Proc. 14th Int. Conf. Ubiquitous Computing ACM. 481--490.

Digital Library

[7]

Yves-Alexandre de Montjoye, Jordi Quoidbach, Florent Robic, and Alex Sandy Pentland. 2013. Predicting personality using novel mobile phone-based metrics. In Social computing, behavioral-cultural modeling and prediction. Springer, 48--55.

Digital Library

[8]

Don A Dillman, Jolene D Smyth, and Leah Melani Christian. 2014. Internet, phone, mail, and mixed-mode surveys: the tailored design method. John Wiley & Sons.

Digital Library

[9]

Wen Dong, Bruno Lepri, and Alex Sandy Pentland. 2011. Modeling the co-evolution of behaviors and social relationships using mobile phone data. In Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia. ACM, 134--143.

Digital Library

[10]

Nathan Eagle and Alex Pentland. 2005. Social serendipity: Mobilizing social software. Pervasive Computing, IEEE 4, 2 (2005), 28--34.

Digital Library

[11]

Nathan Eagle and Alex Pentland. 2006. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10, 4 (2006), 255--268.

Digital Library

[12]

Nathan Eagle and Alex Sandy Pentland. 2009. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology 63, 7 (2009), 1057--1066.

[13]

Nathan Eagle, Alex Sandy Pentland, and David Lazer. 2009. Inferring friendship network structure by using mobile phone data. Proceedings of the national academy of sciences 106, 36 (2009), 15274--15278.

[14]

Salma Elmalaki, Lucas Wanner, and Mani Srivastava. 2015. CAreDroid: Adaptation Framework for Android Context-Aware Applications. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. ACM, 386--399.

Digital Library

[15]

Denzil Ferreira, Vassilis Kostakos, and Anind K Dey. 2015. AWARE: mobile context instrumentation framework. Frontiers in ICT 2 (2015), 6.

[16]

Michal Ficek, Nathaniel Clark, and Lukáš Kencl. 2012. Can crowdsensing beat dynamic cell-ID?. In Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones. ACM, 10.

Digital Library

[17]

Jon Froehlich, Mike Y Chen, Sunny Consolvo, Beverly Harrison, and James A Landay. 2007. MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones. In Proceedings of the 5th international conference on Mobile systems, applications and services. ACM, 57--70.

Digital Library

[18]

R.K. Ganti, F. Ye, and H. Lei. 2011. Mobile Crowdsensing: Current state and Future Challenges. IEEE Communications Magazine 49 (2011), 32--39.

[19]

John Hicks, Nithya Ramanathan, Donnie Kim, Mohamad Monibi, Joshua Selsky, Mark Hansen, and Deborah Estrin. 2010. AndWellness: an open mobile system for activity and experience sampling. In Wireless Health 2010. ACM, 34--43.

Digital Library

[20]

Sibren Isaacman, Richard Becker, Ramón Cáceres, Stephen Kobourov, Margaret Martonosi, James Rowland, and Alexander Varshavsky. 2011. Identifying important places in people's lives from cellular network data. In Pervasive Computing. Springer, 133--151.

Digital Library

[21]

PP Jayaraman, A Sinha, W Sherchan, S Krishnaswamy, A Zaslavsky, P Delir Haghighi, S Loke, and M Thang Do. 2012. Here-n-Now: A Framework for Context-Aware Mobile Crowdsensing. In Proceedings of the Tenth International Conference on Pervasive Computing, UK.

[22]

Kleomenis Katevas, Hamed Haddadi, and Laurissa Tokarchuk. 2014. Poster: Sensingkit: A multi-platform mobile sensing framework for large-scale experiments. In Proceedings of the 20th annual international conference on Mobile computing and networking. ACM, 375--378.

Digital Library

[23]

Nicholas D Lane, Yohan Chon, Lin Zhou, Yongzhe Zhang, Fan Li, Dongwon Kim, Guanzhong Ding, Feng Zhao, and Hojung Cha. 2013. Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. ACM, 7.

Digital Library

[24]

Nicholas D Lane, Ye Xu, Hong Lu, Shaohan Hu, Tanzeem Choudhury, Andrew T Campbell, and Feng Zhao. 2011. Enabling large-scale human activity inference on smartphones using community similarity networks (CSN). In Proceedings of the 13th international conference on Ubiquitous computing. ACM, 355--364.

Digital Library

[25]

Suhas Mathur, Tong Jin, Nikhil Kasturirangan, Janani Chandrasekaran, Wenzhi Xue, Marco Gruteser, and Wade Trappe. 2010. Parknet: drive-by sensing of road-side parking statistics. In Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 123--136.

Digital Library

[26]

Richard P Mattick and J Christopher Clarke. 1998. Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behaviour research and therapy 36, 4 (1998), 455--470.

[27]

Vikas O'Reilly-Shah and Sean Mackey. 2016. Survalytics: An Open-Source Cloud-Integrated Experience Sampling, Survey, and Analytics and Metadata Collection Module for Android Operating System Apps. JMIR mHealth and uHealth 4, 2 (2016), e46.

[28]

Moo-Ryong Ra, Bin Liu, Tom F La Porta, and Ramesh Govindan. 2012. Medusa: A programming framework for crowd-sensing applications. In Proceedings of the 10th international conference on Mobile systems, applications, and services. ACM, 337--350.

Digital Library

[29]

Mashfiqui Rabbi, Shahid Ali, Tanzeem Choudhury, and Ethan Berke. 2011. Passive and in-situ assessment of mental and physical well-being using mobile sensors. In Proceedings of the 13th international conference on Ubiquitous computing. ACM, 385--394.

Digital Library

[30]

K.K. Rachuri., C. Mascolo, M. Musolesi, and P.J. Rentfrow. 2011. Sociablesense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing. In Proceedings of MobiCom. ACM, 73--84.

Digital Library

[31]

Mika Raento, Antti Oulasvirta, and Nathan Eagle. 2009. Smartphones an emerging tool for social scientists. Sociological methods & research 37, 3 (2009), 426--454.

[32]

Sasank Reddy, Deborah Estrin, and Mani Srivastava. 2010. Recruitment framework for participatory sensing data collections. In Pervasive Computing. Springer, 138--155.

Digital Library

[33]

Joel Ross, Lilly Irani, M Silberman, Andrew Zaldivar, and Bill Tomlinson. 2010. Who are the crowdworkers?: shifting demographics in mechanical turk. In CHI'10 extended abstracts on Human factors in computing systems. ACM, 2863--2872.

Digital Library

[34]

Saul Shiffman, Arthur A Stone, and Michael R Hufford. 2008. Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4 (2008), 1--32.

[35]

Adish Singla and Andreas Krause. 2013. Incentives for privacy tradeoff in community sensing. In First AAAI Conference on Human Computation and Crowdsourcing. 165--173.

[36]

Leye Wang, Daqing Zhang, and Haoyi Xiong. 2013. effSense: energy-efficient and cost-effective data uploading in mobile crowdsensing. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. ACM, 1075--1086.

Digital Library

[37]

Leye Wang, Daqing Zhang, Haoyi Xiong, John Paul Gibson, Chao Chen, and Bing Xie. 2016. ecoSense: Minimize Participants' Total 3G Data Cost in Mobile Crowdsensing Using Opportunistic Relays. Systems, Man, and Cybernetics: Systems, IEEE Transactions on (2016), 1--14.

[38]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 3--14.

Digital Library

[39]

Rui Wang, Gabriella Harari, Peilin Hao, Xia Zhou, and Andrew T Campbell. 2015. SmartGPA: how smartphones can assess and predict academic performance of college students. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 295--306.

Digital Library

[40]

Harald Weinschrott, Julian Weisser, Frank Durr, and Kurt Rothermel. 2011. Participatory sensing algorithms for mobile object discovery in urban areas. In Pervasive Computing and Communications (PerCom), 2011 IEEE International Conference on. IEEE, 128--135.

Digital Library

[41]

Yu Xiao, Pieter Simoens, Padmanabhan Pillai, Kiryong Ha, and Mahadev Satyanarayanan. 2013. Lowering the barriers to large-scale mobile crowdsensing. In Mobile Computing Systems and Applications.

Digital Library

[42]

Haoyi Xiong, Daqing Zhang, Guanling Chen, Leye Wang, and Vincent Gauthier. 2015. CrowdTasker: Maximizing coverage quality in Piggyback Crowdsensing under budget constraint. In Pervasive Computing and Communications (PerCom), 2015 IEEE International Conference on. IEEE, 55--62.

Cited By

Langer PAltmüller SFleisch EBarata F(2024)CLAIDFuture Generation Computer Systems10.1016/j.future.2024.05.026159:C(505-521)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.future.2024.05.026
LeBaron VFlickinger TLing DLee HEdwards JTewari AWang ZBarnes L(2023)Feasibility and acceptability testing of CommSense: A novel communication technology to enhance health equity in clinician–patient interactionsDIGITAL HEALTH10.1177/205520762311849919Online publication date: 11-Jul-2023
https://doi.org/10.1177/20552076231184991
Wang ZLarrazabal MRucker MToner EDaniel KKumar SBoukhechba MTeachman BBarnes L(2023)Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious IndividualsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109167:3(1-26)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610916
Show More Cited By

Index Terms

Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

Recommendations

Internet of Music Things: an edge computing paradigm for opportunistic crowdsensing

Device centric music computation in the era of the Internet is participant-centric data recognition and computation that includes devices such as smartphones, real sound sensors, and computing systems. These participatory devices enhance the progression ...
PEAR: A Privacy-Enabled Architecture for Crowdsensing
RACS '17: Proceedings of the International Conference on Research in Adaptive and Convergent Systems

Crowdsensing systems are providing solutions to the community in areas such as transportation, security, entertainment and the environment by citizens who use their consumer devices such as cellphones, wearables, and Internet of Things devices to ...
We want more: human-computer collaboration in mobile social video remixing of music concerts
CHI '11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Recording and publishing mobile video clips from music concerts is popular. There is a high potential to increase the concert's perceived value when producing video remixes from individual video clips and using them socially. A digital production of a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2016

1288 pages

ISBN:9781450344616

DOI:10.1145/2971648

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Copyright © 2016 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UbiComp '16

Sponsor:

UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

Acceptance Rates

UbiComp '16 Paper Acceptance Rate 101 of 389 submissions, 26%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

91
Total Citations
View Citations
2,123
Total Downloads

Downloads (Last 12 months)195
Downloads (Last 6 weeks)22

Reflects downloads up to 04 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Langer PAltmüller SFleisch EBarata F(2024)CLAIDFuture Generation Computer Systems10.1016/j.future.2024.05.026159:C(505-521)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.future.2024.05.026
LeBaron VFlickinger TLing DLee HEdwards JTewari AWang ZBarnes L(2023)Feasibility and acceptability testing of CommSense: A novel communication technology to enhance health equity in clinician–patient interactionsDIGITAL HEALTH10.1177/205520762311849919Online publication date: 11-Jul-2023
https://doi.org/10.1177/20552076231184991
Wang ZLarrazabal MRucker MToner EDaniel KKumar SBoukhechba MTeachman BBarnes L(2023)Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious IndividualsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109167:3(1-26)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610916
Nishiyama YSezaki K(2023)Smartwatch-Based Sensing Framework for Continuous Data Collection: Design and ImplementationAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3612874(620-625)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3612874
Gross TMalzhacker T(2023)The Experience Sampling Method and its Tools: A Review for Developers, Study Administrators, and ParticipantsProceedings of the ACM on Human-Computer Interaction10.1145/35932347:EICS(1-29)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3593234
Cho SKim YJang JHwang I(2023)AI-to-Human ActuationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808127:1(1-32)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580812
Mendes JSilva FCardoso AMoura ICoutinho LViana DEndler MTeles A(2023)OpenDPMH: A Framework for Developing Mobile Sensing Applications of Digital Phenotyping2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS58004.2023.00216(198-203)Online publication date: Jun-2023
https://doi.org/10.1109/CBMS58004.2023.00216
Zhang ETrujillo RTempleton JPoellabauer C(2023)A Study on Mobile Crowd Sensing Systems for Healthcare ScenariosIEEE Access10.1109/ACCESS.2023.334215811(140325-140347)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3342158
Menghini LPastore MBalducci C(2023)Workplace Stress in Real TimeEuropean Journal of Psychological Assessment10.1027/1015-5759/a00072539:6(424-432)Online publication date: Nov-2023
https://doi.org/10.1027/1015-5759/a000725
Suhag DJha V(2023)A comprehensive survey on mobile crowdsensing systemsJournal of Systems Architecture10.1016/j.sysarc.2023.102952142(102952)Online publication date: Sep-2023
https://doi.org/10.1016/j.sysarc.2023.102952
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents