Mobile Phone Based Sensing Software
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Mobile phone–based sensing software is a class of software for mobile phones that uses the phone's sensors to acquire data about the user. Some applications of this software include mental health and overall wellness monitoring. This class of software is important because it has the potential of providing a practical and low-cost approach to deliver psychological interventions for the prevention of mental health disorders, as well as bringing such interventions to populations that have no access to traditional health care. A number of terms are used for this approach, including "personal sensing", "digital phenotyping", and "context sensing". The term "personal sensing" is used in this article, as it conveys in simple language the aim of sensing personal behaviors, states, and conditions.


General information

This article presents a comparison of mobile phone software that can acquire users' sensor data (in a passive manner without users' explicit intervention) and administer questionnaires (or micro-surveys triggered by sensor events). The software described below helps quantify behaviors known to be related to mental health and wellness. The list below includes both commercial and free software. To be included in this list, a software product must be able to acquire data from at least one phone sensor, and provide a minimum level of security for storage and transmission of acquired data. This list excludes software that focuses solely on collecting participant data from surveys and questionnaires.


Software table

The following table contains general information about each mobile-based sensing software, such as who the developers are, when it was last updated, whether it is open or closed source, and the programming language and database they are based on.


Target audience

The following table shows the target audience for each piece of software included in this article. Software packages that target developers assume a high level of skill in creating code and/or modifying third-party source code. Software packages that target researchers have at least one component that can be used in scientific studies with human subjects. Software packages that target individuals allow at least one component to be downloaded and installed by an end-user with no programming skills. Please note that some packages target more than one type of user.


Mobile OS support

The following table shows the type of mobile phone on which each software package can be deployed.


Installation

In addition to deploying mobile-based sensing software to smart phones, a control dashboard has to be either installed on a local computer or provided through the web. Some of the packages provide a web server so that one is able to have a remote dashboard. The table below shows the server platform and/or web server required for each piece of software.


Sensor (and other) data that can be captured (part 1)

The following table shows the types of mobile sensors from which each software package is capable of collecting sensor data. Note that the type of data collected depends on availability of the appropriate sensor hardware on a specific smartphone. Some software packages collect raw sensor data (e.g. Beiwe) whereas others collect summaries of such data (e.g. ResearchKit).


Sensor and data that can be captured (part 2)

The following table shows the types of mobile sensors from which each software package is capable of collecting passive data. Note that the type of data collected depends on availability of the appropriate sensor on the smartphone.


Support for behavioral studies

The following table contains information regarding availability of functions, within each software package, that support behavioral experiments for scientific purposes.


Battery management

The following table contains information relative to battery management for each software package. As passive data collection from smartphone sensors is a battery-intensive process, methods to maximize battery performance are important for this type of software.


Software maintenance and support

The following table contains information relative to maintenance and support for each software package. The information provided in this table gives an idea of the likelihood of a package to be supported in the future.


Security and privacy

The following table contains information relative to encryption and secure transfer of data collected from smartphone sensors. This information is very important for a data collection app due to privacy concerns over the handling of phone data.


Cost

The following table contains information relative to whether a software package is free or non-free.


See also

*
MHealth mHealth (also written as m-health or mhealth) is an abbreviation for mobile health, a term used for the practice of medicine and public health supported by mobile devices. The term is most commonly used in reference to using mobile communication ...
* Quantifield self (QS) * Ecological momentary assessment (EMA) * Event sampling methodology (ESM) *
Diary studies A diary is a written or audiovisual record with discrete entries arranged by date reporting on what has happened over the course of a day or other period. Diaries have traditionally been handwritten but are now also often digital. A personal d ...
*
Digital phenotyping Digital phenotyping is a multidisciplinary field of science, first defined in a May 2016 paper in ''JMIR Mental Health'' authored by John Torous, Mathew V Kiang, Jeanette Lorme, and Jukka-Pekka Onnela as the “moment-by-moment quantification of t ...


Notes and references

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Poster: SensingKit: a multi-platform mobile sensing framework for large-scale experiments; Kleomenis Katevas, Hamed Haddadi, Laurissa Tokarchuk; Published in: Proceeding MobiCom '14 Proceedings of the 20th annual international conference on Mobile computing and networking; Pages 375-378; Maui, Hawaii, USA — September 07–11, 2014
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ResearchKit provides data collection in two ways: (1) through predefined macros for detection of active tasks, where each task state is extracted from information obtained through a combination of phone sensors (please refer to table a

; and (2) through the iOS HealthKit and CoreMotion (https://developer.apple.com/documentation/coremotion developer.apple.com/documentation/coremotion) APIs.
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emotionsense.github.io/sensors.html
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The empath platform's sensors are external to the phone. Rather than giving access to specific phone sensors, context Sensing SDK provides access to "context states", each one of those states drawing data from combination of sensors. We have listed here the sensors and not the states referred to in the developers manual located a
software.intel.com/en-us/documentation/context-sensing-sdk-for-android-states-datasheet
for Android and a
software.intel.com/en-us/documentation/context-sensing-sdk-for-windows-states-datasheet
for Windows mobile. Retrieved July 1, 2017.
tech.cbits.northwestern.edu/2013/10/04/purple-robot-importer-purple-robot-warehouse
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Retrieved June 29, 2017.
Context Sensing SDK online documentation
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Context Sensing SDK download page
Retrieved June 29, 2017.
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/ref> Empath: a continuous remote emotional health monitoring system for depressive illness; Robert F. Dickerson, Eugenia I. Gorlin, John A. Stankovic; WH '11 Proceedings of the 2nd Conference on Wireless Health; Article No. 5; San Diego, California — October 10–13, 2011
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/ref> CenceMe – Injecting Sensing Presence into Social Networking Applications; Miluzzo E., Lane N.D., Eisenman S.B., Campbell A.T. (2007); In: Kortuem G., Finney J., Lea R., Sundramoorthy V. (eds) Smart Sensing and Context. EuroSSC 2007; Lecture Notes in Computer Science, vol 4793. Springer, Berlin, Heidelber
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Personal communication.{{full citation needed, date=April 2021 tech.cbits.northwestern.edu/purple-robot
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ttps://awareframework.slack.com awareframework.slack.com Retrieved June 24, 2017 Social fMRI: Investigating and shaping social mechanisms in the real world; Nadav Aharony, Wei Pan, Cory Ip, Inas Khayal, Alex Pentland; Pervasive and Mobile Computing (2011)
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/ref> “SensingKit: Evaluating the Sensor Power Consumption in iOS devices”; Kleomenis Katevas, Hamed Haddadi and Laurissa Tokarchuk; 12th International Conference on Intelligent Environments (IE'16); September 2016; London, UK
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/ref> New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research; Torous J, Kiang MV, Lorme J, Onnela JP; JMIR Ment Health (2016);3(2):e1
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/ref> Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders; Place S, Blanch-Hartigan D, Rubin C, Gorrostieta C, Mead C, Kane J, Marx BP, Feast J, Deckersbach T, Pentland A, Nierenberg A, Azarbayejani A; J Med Internet Res (2017);19(3):e7
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/ref> An open source mobile platform for psychophysiological self tracking; Gaggioli A, Cipresso P, Serino S, Pioggia G, Tartarisco G, Baldus G, Corda D, Riva G; Stud Health Technol Inform. (2012);173:136-
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/ref> , 2 {{cite web , url=http://wiki.beiwe.org/wiki/ , title=Beiwe Wiki , website=wiki.beiwe.org , access-date=January 19, 2018 {{cite web , url=https://onnelalab.slack.com/ , title= , website=onnelalab.slack.com , access-date=January 19, 2018{{title missing, date=May 2022 Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions. Boonstra TW, Nicholas J, Wong QJ, Shaw F, Townsend S, Christensen H (2018) J Med Internet Res 20(7):e10131

/ref> Lind, M. N., Byrne, M. L., Wicks, G., Smidt, A. M., & Allen, N. B. (2018). The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing. JMIR Mental Health, 5(3), e10334–10

/ref> Mobile software