Mobile Crowdsensing in Healthcare Scenarios: Taxonomy, Conceptual Pillars, Smart Mobile Crowdsensing Services

Recently, new paradigms like crowdsensing emerged in the context of mobile technologies that promise to support researchers in life sciences and the healthcare domain in a new way. For example, by the use of smartphones, valuable data can be quickly gathered in everyday life and then easily compared to other crowd users, especially when taking environmental factors or sensor data additionally into account. In the context of chronic diseases, mobile technology can particularly help to empower patients in coping with their individual health situation more properly. However, to utilize the achievements of mobile technology in the aforementioned contexts is still challenging. Following this, the work at hand discusses two important and relevant aspects for mobile crowdsensing in healthcare scenarios. First, the status quo of mobile crowdsensing technologies and their relevant perspectives on healthcare scenarios are discussed. Second, salient aspects are presented, which can help researchers to conceptualize mobile crowdsensing to a more generic software toolbox that is able to utilize data gathered with smartphones and their built-in sensors in everyday life. The overall toolbox goal is the support of researchers to conduct studies or analyzes on this new and less understood kind of data source. On top of this, patients shall be empowered to demystify their individual health condition more properly when using the toolbox, especially by exploiting the wisdom of the crowd.

[1]  Manfred Reichert,et al.  Mobile Crowdsensing for the Juxtaposition of Realtime Assessments and Retrospective Reporting for Neuropsychiatric Symptoms , 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).

[2]  M. Reichert,et al.  Entwicklung mobiler Apps: Konzepte, Anwendungsbausteine und Werkzeuge im Business und E-Health , 2015 .

[3]  J. Os,et al.  Experience sampling research in psychopathology: opening the black box of daily life , 2009, Psychological Medicine.

[4]  Harald Baumeister,et al.  Feasibility of Linking Molecular Genetic Markers to Real-World Social Network Size Tracked on Smartphones , 2018, Front. Neurosci..

[5]  Murat Ali Bayir,et al.  Crowd-sourced sensing and collaboration using twitter , 2010, 2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[6]  Salil S. Kanhere,et al.  A survey on privacy in mobile participatory sensing applications , 2011, J. Syst. Softw..

[7]  Merkourios Karaliopoulos,et al.  User recruitment for mobile crowdsensing over opportunistic networks , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[8]  Lei Shu,et al.  When Mobile Crowd Sensing Meets Traditional Industry , 2017, IEEE Access.

[9]  Myra Spiliopoulou,et al.  Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform , 2019, International Journal of Data Science and Analytics.

[10]  Manfred Reichert,et al.  Mobile Crowdsensing Services for Tinnitus Assessment and Patient Feedback , 2017, 2017 IEEE International Conference on AI & Mobile Services (AIMS).

[11]  H. Baumeister,et al.  Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology , 2019, J.

[12]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[13]  Athanasios V. Vasilakos,et al.  Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles , 2016, Sensors.

[14]  Rüdiger Pryss,et al.  Emotional states as mediators between tinnitus loudness and tinnitus distress in daily life: Results from the “TrackYourTinnitus” application , 2016, Scientific Reports.

[15]  Manfred Reichert,et al.  Entwicklung mobiler Apps , 2015 .

[16]  Manfred Reichert,et al.  Design and Implementation of a Scalable Crowdsensing Platform for Geospatial Data of Tinnitus Patients , 2019, 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS).

[17]  Manfred Reichert,et al.  Does Tinnitus Depend on Time-of-Day? An Ecological Momentary Assessment Study with the “TrackYourTinnitus” Application , 2017, Front. Aging Neurosci..

[18]  Manfred Reichert,et al.  Mobile Crowd Sensing Services for Tinnitus Assessment, Therapy, and Research , 2015, 2015 IEEE International Conference on Mobile Services.

[19]  Huadong Ma,et al.  Opportunities in mobile crowd sensing , 2014, IEEE Communications Magazine.

[20]  M. Reichert,et al.  Die KINDEX-App - ein Instrument zur Erfassung und unmittelbaren Auswertung von psychosozialen Belastungen bei Schwangeren in der täglichen Praxis bei Gynäkologinnen, Hebammen und in Frauenkliniken , 2016, Verhaltenstherapie.

[21]  Fan Wu,et al.  Sustainable Incentives for Mobile Crowdsensing: Auctions, Lotteries, and Trust and Reputation Systems , 2017, IEEE Communications Magazine.

[22]  Manfred Reichert,et al.  Requirements for a Flexible and Generic API Enabling Mobile Crowdsensing mHealth Applications , 2018, 2018 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems (RESACS).

[23]  Thomas Kubiak,et al.  Psychological and Psychophysiological Ambulatory Monitoring A Review of Hardware and Software Solutions , 2007 .

[24]  Manfred Reichert,et al.  Development of Mobile Data Collection Applications by Domain Experts: Experimental Results from a Usability Study , 2017, CAiSE.

[25]  Manfred Reichert,et al.  Towards Incentive Management Mechanisms in the Context of Crowdsensing Technologies based on TrackYourTinnitus Insights , 2018, FNC/MobiSPC.

[26]  Yu Huang,et al.  Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies , 2016, UbiComp.