Mobile phone based sensing software
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.
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.
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.
This section's factual accuracy may be compromised due to out-of-date information. (July 2017)
|Name||Creator||Latest release||Latest release date||Software license||Open source||Programming language||Data backend|
|Beiwe Research Platform||HSPH Onnela Lab||1.3.0 (iOS)
|Dec 21 2017 iOS
April 2018 Android
|CenceMe||Smartphone Sensing Group, Darmouth College||1.2.0 (iOS)||July 27, 2009 (iOS)||Unknown||No||Python, Perl, PHP, Bash||MySQL|
|Context Sensing SDK||Intel||v1.0-beta2 (Windows Mobile); v1.8 (Android)||March 18, 2016 (Android)||Intel EULA||Yes, available to download from Intel's website||Android: Java; Windows Mobile: C#||Flexible|
|EARS||Center for Digital Mental Health||Android 2.28; iOS 1.7.0||May 2020||Apache License 2.0||Yes||Android: Java; iOS: Swift||Amazon Web Services|
|Empath||Department of Computer Science, University of Virginia||Unknown||2011||Unknown||No||Unknown||MySQL|
|Expimetrics||Louis Tay, Purdue University||Unknown||Unknown||Proprietary||No||Unknown||Unknown|
|Emotion Sense||Department of Psychology, University of Cambridge||Beta||April 25, 2017||BSD Licenses||Yes||Java||SQLite|
|Funf Open Sensing Framework||Originally at MIT Media Lab, now maintained by Behavio (Google)||v0.5.0RC1||September 4, 2015||LGPL||Yes||Java||SQLite|
|mindLAMP Platform||Division of Digital Psychiatry @ Beth Israel Deaconess Medical Center||v2.0||August 27th, 2020||BSD-3||Yes||TypeScript, Python, R, Swift, Kotlin||Flexible (noSQL)|
|Mobile Sensing Platform||Cogito Corporation||Unknown||Unknown||Proprietary||No||Unknown||Unknown|
|movisensXS||Movisens GmbH||1.1.1||January 5, 2017||Apache License 2.0||Partial||Java, Kotlin||Unknown|
|Psychlog||Instituto Auxologico Italiano and National Research Council of Italy||Beta||April 11, 2013||License-free software||Yes||C#||MySQL|
|Purple Robot||CBITS, Northwestern University||2.0.8||November 3, 2015||GPLv3||Yes||Java||PostgreSQL|
|Radar-CNS||King's College and Janssen Pharmaceutica NV||2.0.3-beta (dashboard); 0.1-alpha.2 (Android)||June 23, 2017 (dashboard); April 13, 2017 (Android)||Apache License 2.0||Yes||Android: Java, Web: Typescript||MongoDB|
|RealLife exp||LifeData LLC||Unknown||Unknown||Proprietary||No||Unknown||Unknown|
|ResearchKit||Apple||1.5||June 7, 2017||BSD||Yes||Objective-C, Swift||Flexible, can use Sage Bridge  to build own backend|
|Research Stack||Cornell Tech's Small Data Lab and open mHealth||1.1.1||November 18, 2016||Apache License 2.0||Yes||Java||SQLite, but can also use Sage Bridge  to build own backend|
|SensingKit||Queen Mary University of London||0.5.1 (iOS), 0.2.0 (Android)||September 20, 2018 (iOS) July 29, 2015 (Android)||LGPL||Yes||Objective-C, Swift, Java||Depends on implementation|
|Socialise||Black Dog Institute||v0.2||June 16, 2017||Proprietary||No||iOS: Objective-C, Android: Java||Unknown|
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.
|Beiwe Research Platform||Yes||Yes||Yes|
|Context sensing SDK||Yes||No||No|
|Mobile Sensing Platform||No||Yes||No|
|Passive Data Kit||Yes||Yes||Yes|
Mobile OS support
The following table shows the type of mobile phone on which each software package can be deployed.
|Beiwe Research Platform||Yes||Yes||No||No|
|Mobile Sensing Platform||Yes||No||No||No|
|Passive Data Kit||Yes||Yes||No||No|
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.
|Name||Server platform (operating system or web)||Web server required|
|AWARE||Web||Aware provides both a server hosted by them or the ability to host dashboard on own server|
|Beiwe Research Platform||Web||System back-end, web server, data storage on AWS|
|Context sensing||Web / Windows / Mac||Depends on application|
|EARS||Web||Data storage on AWS|
|Emotion sense||Web||Depends on configuration|
|mindLAMP Platform||Web||Depends on application|
|mEMA||Web||Illumivu provides a web server for a fee|
|Metricwire||Web||Metricwire provides web server for a fee|
|Mobile Sensing Platform||Unknown||Unknown|
|MovisensXS||Web||Not required. Server hosted in ISO 27001 certified, German Data Center|
|Murmuras||Web||Not required. Own hardware servers colocated in Germany.|
|Psyt||Web||Not required. Server is hosted by Psyt|
|Radar-CNS||Frontend dashboard app||Yes|
|RealLife Exp||Web||LifeData provides a web server for a fee|
|Research Stack||Web server||Yes|
|SensingKit||Web server||Depends on application|
|unforgettable.me||Web||Web server on Amazon EC2, Data storage on Amazon S3, Cloudsearch|
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).
|AWARE||Yes||Yes||Yes||Yes||No||Yes||Yes||Yes (Android only)||Yes||Yes|
|Beiwe Research Platform||Yes||No||Yes||Yes (Android only)||No||No||Yes (iOS only)||No||Yes (iOS only)||Yes|
|Context sensing||Yes||No||Yes||No||No||No||Yes||Yes, Windows mobile only||No||Yes|
|EARS||Yes||No||Yes||No||No||No||Yes||Yes (Android only)||No||Yes|
|Mobile Sensing Platform||Yes||No||Yes||No||No||No||Yes||No||No||No|
|Murmuras||not provided but possible||not provided but possible||not provided but possible||not provided but possible||No||No||not provided but possible||not provided but possible||not provided but possible||not provided but possible|
|Passive Data Kit||Yes||Unknown||Unknown||Unknown||Unknown||Unknown||Unknown||Yes||Unknown||Unknown|
|ResearchKit||Yes||Yes||not provided but possible||not provided but possible||No||not provided but possible||Yes||not provided but possible||Yes||not provided but possible|
|Research Stack||Mobile sensor input provided through SensingKit (below)|
|SensingKit||Yes||Yes, iOS only||Yes||Yes, Android only||No||Yes||Yes||Yes, Android only||Yes||No|
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.
|Name||Linear accelerometer||Location||Audio||Pressure sensor||Proximity||Rotation||Surveys||Temperature||Network reachability|
|AWARE||Yes||Yes||Yes||No||Yes (Android only)||Yes||Yes||Yes (Android only)||Yes|
|Beiwe Research Platform||No||Yes||Yes||No||Yes (iOS only)||No||Yes||No||Yes|
|Context sensing||No||Yes||Yes||No||Yes, Windows Mobile only||Yes||No||No||Yes|
|Mobile Sensing Platform||No||Yes||Yes||No||No||No||No||No||No|
|Murmuras||not provided but possible||Yes||not provided but possible||No||not provided but possible||not provided but possible||Time and app usage triggered ESM/EMA||not provided but possible||not provided but possible|
|Passive Data Kit||Unknown||Unknown||Unknown||Unknown||Unknown||Unknown||Unknown||Unknown||Unknown|
|Psyt||Custom||Yes||Yes||Yes||Custom||No||Specialize in ESM and EMA surveys||No||Yes|
|ResearchKit||not provided but possible||Yes||Yes||Yes||not provided but possible||Yes||Yes||not provided but possible||not provided but possible|
|Research Stack||Mobile sensor input provided through SensingKit (below)|
|SensingKit||Yes||Yes||Yes||No||Yes, only on iOS||Yes||No||No||No|
Support for behavioral studies
The following table contains information regarding availability of functions, within each software package, that support behavioral experiments for scientific purposes.
|Name||How does data get from phone to database?||Can surveys be triggered by phone sensors?||Can surveys be triggered remotely by investigator?||Can sensor data config. be remotely changed?||Can platform monitor data gaps and alert investigator?||Does platform support running scripts on phone?|
|AWARE||Sensor data is uploaded to an AWARE Server instance (hosted or self-hosted) when online (WiFi only or any available connection)||Yes||Yes||Yes||Unknown||Unknown|
|Beiwe Research Platform||Wifi or cellular||No||Yes||Yes||Depends on implementation||No|
|Context sensing||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation|
|EARS||Sensor data is uploaded to AWS when connected to Wifi or Mobile Data.||Yes||Yes||Yes||Yes||Unknown|
|Empath||Remote server connection||Unknown||Unknown||Unknown||Unknown||Unknown|
|Emotion sense||Wifi||Yes, by using the trigger library||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation|
|Funf||(1) manual export or Android file transfer service, (2) manual transfer from device's memory card, (3) setting up server and configure funf to upload data to server.||No||No||Yes||Depends on implementation||Depends on implementation|
|mindLAMP Platform||WiFi and Cellular Data||Depends on implementation||Yes||Depends on implementation||Depends on implementation||Yes|
|Mobile Sensing Platform||Unknown||Unknown||Unknown||Unknown||Unknown||Unknown|
|MovisensXS||Cell or Wifi. Online and Offline||Yes||No||Yes||Yes||No|
|Murmuras||WiFi or cellular.||Yes||Yes||Yes||Depends on implementation||Depends on implementation|
|Passive Data Kit||Unknown||Unknown||Unknown||Unknown||Unknown||Unknown|
|Psyt||Hosted data store; automatic over-the-air background pload||Yes||In development||Yes||Yes||Yes|
|Purple Robot||Sensor data and user provided information is securely transmitted to custom data warehouses||Yes||Unknown||Yes||Unknown||Unknown|
|ResearchKit||Wi-fi or cellular||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation|
|Research Stack||Wi-fi or cellular||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation|
|SensingKit||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation||Depends on implementation|
|Socialise||Wi-fi or cellular||No||Yes||No||Unknown||Unknown|
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.
|Name||Relative drain on battery||Methods of managing battery life|
|AWARE||Overall battery impact on average: 19.7mA when sensing only; 24.7mA when storing locally; and 138mA when connected to server||Built-in location algorithm that minimizes battery drain. Also uses event based sampling, opportunistic analysis and scheduled synching to reduce battery consumption|
|Beiwe Research Platform||Internal testing of Beiwe did not result in significant battery drain ||Battery drain depends entirely on data collection settings|
|EARS||Rates of battery drainage are affected by which sensors are activated. The EARS Android app drains around 0.38% of the battery of a Samsung Galaxy 7 every hour of collection on all sensors. On newer devices or devices with fewer sensors, a smaller percentage is drained every hour.||Battery drain depends entirely on data collection settings|
|Emotion sense||Unknown||To extend battery life, Emotionsense offloads computations to a remote server|
|Funf||Unknown||nonspecified type of "Battery optimization"|
|mindLAMP Platform||Minor battery drain, approximately ~0.5%/hour depending on device and collection settings.||Battery drain depends entirely on data collection settings|
|Mobile Sensing Platform||Unknown||Unknown|
|MovisensXS||Continues location tracking results in 4 days of battery life||Special algorithms maintain a low battery usage|
|Murmuras||Testing of Murmuras did not result in significant battery drain.||Battery drain depends entirely on data collection settings.|
|Passive Data Kit||Unknown||Unknown|
|Psychlog||5–6 hours of autonomy||None|
|Psyt||Varies with sensor implementation||No drain when backgrounded on iOS|
|Purple Robot||The battery drain (in seconds per %) of old uploader plugin was 211 seconds; after introducing a new uploader, the battery drain was 584 seconds||In 2014 purplerobot introduced optimizations that increased battery life 176%|
|ResearchKit||Depends on implementation||Depends on implementation|
|Research Stack||Depends on implementation||Depends on implementation|
|SensingKit||Battery performance was measured on an iPhone 5S running iOS 9.2 and the battery had the following duration performance: idle (51hrs), accelerometer (31hrs), gyroscope (28hrs), magnetometer (34hrs), device motion (21hrs), location (18hrs)||Unknown|
|Socialise||Battery performance was assessed on participants' own devices. Average battery life was 21.3 hours when app was not scanning and 18.8 hours when GPS, Bluetooth and battery data was collected every 5 minutes||Unknown|
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.
|Name||Online documentation available||User's forum / technical support||How actively is software maintained?||User base||Support for bugs and updates||Location of source code (github, sourceforge, bitbucket, launchpad)?||Funding source|
|AWARE||Yes||Slack, 503 users as of Oct 29, 2019||Android client: actively maintained (Denzil Ferreira); iOS client: actively maintained (Yuuki Nishiyama)||Used in 2722 studies across institutions worldwide.||Slack and GitHub Issue tracking actively used.||GitHub||Academy of Finland; Tekes; EU (Marie Curie Actions)|
|Beiwe Research Platform||Yes||Slack||6 iOS releases, 14 Android releases||Beiwe is currently used in 25 studies, primarily at various Harvard Medical School teaching hospitals||Yes||GitHub||NIH Director's New Innovator Award|
|Cenceme||No||No||Unknown||Unknown||Unknown||Unknown||Intel, Nokia, NSF, DHS|
|Context sensing SDK||Yes||Intel Context sensing SDK developer's forum at Intel.com||Unknown||Unknown||From May 18, 2016 to July 3, 2017, there were two issues posted the developers forum, one of then had 2 replies from intel and the other one had one reply from intel staff||Intel's Context Sensing SDK's download page||Intel|
|Emotion sense||Yes||Developers Forum at Google Groups||The sensor manager for Android had 0 commits from the week of July 10, 2016 to July 3, 2017. The Android sensor data manager had 0 commits from the week of July 10, 2016 to July 3, 2017. The iOS survey manager had 0 commits from the week of July 10, 2016 to July 3, 2017. The iOS sensor manager has 0 commits from the week of July 10, 2016 to July 3, 2017.||33 topics at their Google Group's developers forum. From July 3, 2016 to July 3, 2017 there were 5 topics opened, for a total of 11 posts. The forum seems to have gone silent since March 2017.||The developers forum seems to have gone silent since March 2017 and no issues have been raised in the github page in the last year (July 3, 2016 to July 3, 2017).||GitHub||EPSRC (funding expired in 2015)|
|Funf||Yes||Developer's forum at Google Groups||Funf open sensing framework Android library and 0 commits from week of July 10, 2016 to July 3, 2017. The processing data scripts repository had 0 commits from week of July 10, 2016 to July 3, 2017.||The Funf developers google group has 338 members and a total of 229 topics, the large majority of which were started before the last year (July 3, 2016 to July 3, 2017).||A total of 60 issues were reported on the Funf open sensing framework at github, 52 of which were resolved. No issues have been reported in the last year (July 3, 2016 to July 3, 2017).||GitHub||The Knight Foundation, Google|
|mindLAMP Platform||Yes||Yes||Frequent updates, highly maintained.||style="background:#9F9;vertical-align:middle;text-align:center;" class="table-yes"|Yes||GitHub||Public|
|Mobile Sensing Platform||No||Unknown||Unknown||Unknown||Unknown||Unknown||DARPA|
|MovisensXS||Yes||Technical Support||Highly maintained. Every Android update is prepared. ~ Quarterly Updates.||~1700 Researchers. ~6000 Studies||Intercom 24/7 Chat support and Email.||Not available||Private|
|Murmuras||No||Technical Support||Frequent updates, highly maintained.||Unknown||Email and Phone support depending on contract.||Private||Commercial|
|Passive Data Kit||Coming soon||No users or developers forum||In the Passive data kit for Android there have been 64 commits from the week of July 10, 2016 to July 3, 2017. In the passive data kit for iOS there have been 19 commits from the week of July 10, 2016 to July 3, 2017. In the passive data kit online server there have been 75 commits from the week of July 10, 2016 to July 3, 2017||Unknown||Unknown||GitHub||Private|
|Psychlog||No||None||Not available||Not available||Not available||Sourceforge||EU funding expired 2013|
|Psyt||No||Email support group||>1500 commits per year||Eight research projects, over 100,000 research participants||Fully supported under contract (for iOS and whitelisted Android devices)||Private||Commercial|
|Purple Robot||Yes||No users or developers forum available||There has been 0 commits from the week of July 10, 2016 to July 3, 2017.||Purple robot has been forked 20 times and it has 7 contributors and 35 branches||There are 44 issues open and 225 resolved. None of the reported issues has been within the last year (July 3, 2016 to July 3, 2017)||GitHub||Unknown|
|Radar-CNS||No||No users or developers forum available||The questionnaire mobile application code had 111 commits from the week of July 10, 2016 to July 3, 2017 (4 contributors). The dashboard source code had 290 commits from the week of July 10, 2016 to July 3, 2017 (4 contributors). The functionality for Android passive plugins had 372 commits from the week of July 10, 2016 to July 3, 2017 (6 contributors). The phone sensor plugin for passive remote monitoring app had 301 commits from the week of July 10, 2016 to July 3, 2017 (6 contributors). The android app source code had 139 commits from the week of July 10, 2016 to July 3, 2017 (5 contributors).||The repositories contained within Radar-CNS github's account have been forked 20 times, which might be an indicator of the user/developer base||Questionnaire source code has a total of 13 open issues and 13 closed issues. The dashboard source code has a total of 25 open issues and 11 issues closed. The functionality for Android passive plugins has a total of 6 issues open and 3 closed. The phone sensor plugin has 0 total issues. The android app code has 13 issues open and 14 closed.||GitHub||Horizon 2020 and EFPIA|
|RealLife Exp||Yes||Help center||Not available||Not available||Not available||Not available||Private|
|ResearchKit||Yes||Developers Forum at Apple Developer Forums||200 commits from the week of July 10, 2016 to July 2, 2017 (96 contributors).||6 topics related to Researchkit within the developers forum, all of them opened within the last year (July 2016 to July 2017).||72 issues open and 362 closed (about a third of those issues occurred within the last year).||GitHub||Apple|
|Research Stack||Yes||Developers Forum at Google Groups||14 commits from the week of July 10, 2016 to July 2, 2017 (14 contributors)||32 topics in the developers forum, 11 of those topics updated within the last year (July 3, 2016 to July 2, 2017).||52 issues open and 78 closed. About a third of those issues occurred within the last year (July 2016 to July 2017).||GitHub||Robert Wood Johnson Foundation|
|SensingKit||Yes||No users or developers forum available||Sensingkit for iOS source code had 282 commits from the week of July 10, 2016 to July 2, 2017 (1 contributor).Sensingkit for Android had no commits from the week of July 10, 2016 to July 2, 2017 (1 contributor). Sensingkit server platform had no commits from the week of July 10, 2016 to July 2, 2017 (1 contributor)||15 forks total in Sensingkit for Android, iOS and server||There was a total of 3 issues open and 6 closed in iOS, Android and server repositories combines, all of the issues occurred within the last year (July 3, 2016 to July 2, 2017).||GitHub||UK Defence Science and Technology Laboratory|
|Socialise||No||No||Unknown||Unknown||Unknown||Private||National Health and Medical Research Council|
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.
|Name||Database encryption?||Secure data transfer?|
|AWARE||Locally protected by application signature and permission; enabled remotely by MySQL server deployment||SHA-256 with a 2048-bit long RSA strong encryption key or encrypted with any SSL certificate|
|Beiwe Research Platform||All data on phones, on the server, and in-transit use industry-standard encryption techniques. Data on phones and server are encrypted using 2048 bit RSA encryption and AES||SSL Encrypted connection|
|Context sensing||Not provided but can be accomplished using other APIs||Not provided but can be accomplished using other APIs|
|EARS||AES public-private key pairing on device + AWS encryption||TLS (1.2 or greater) Encrypted connection|
|Emotion sense||Encryption is used but was unable to obtain more details||Encryption is used but was unable to obtain more details|
|Funf||RSA||Depends on type of transfer but data is encoded using RSA|
|mindLAMP Platform||AES public-private key pairing on device + AWS encryption||TLS (1.2 or greater) Encrypted connection|
|Mobile Sensing Platform||AES, public-private key pairing||SSL encrypted connection|
|MovisensXS||AES, public-private key pairing||SSL encrypted connection|
|Murmuras||Locally protected by application signature and permission.||TLS/SSL with Certificate pinning|
|Passive Data Kit||Unknown||SSL|
|Psyt||On device (varies by platform) and back-end (Amazon Web Services RDS Postgres encrypted database)||Qualys A+ rated TLS|
|Radar-CNS||Encryption not provided yet||Encryption not provided yet|
|RealLife Exp||AES 256 bit encryption||Web app to server transmission: synchronous and asynchroonus SSL encryption; mobile app to server transmission: encoded using Base64 and encrypted using AES 256 bit encryption prior to transmission|
|ResearchKit||Not provided, thus it is the responsibility of the developer||Encrypted communication between app and server not provided|
|Research Stack||AES||Can use Sage Bridge|
|SensingKit||Not provided, thus it is the responsibility of the developer||Not provided, thus it is the responsibility of the developer|
The following table contains information relative to whether a software package is free or non-free.
|Beiwe Research Platform||Free|
|Context sensing SDK||Free|
|Mobile Sensing Platform||Unknown|
|MovisensXS||Free for students|
|Murmuras||Free for students|
|Passive Data Kit||Free|
- Quantifield self (QS)
- Ecological momentary assessment (EMA)
- Event sampling methodology (ESM)
- Diary studies
- Digital phenotyping
Notes and references
- Prevention of Mental Health Disorders Using Internet- and Mobile-Based Interventions: A Narrative Review and Recommendations for Future Research. Ebert DD, Cuijpers P, Muñoz RF and Baumeister H (2017) Front. Psychiatry 8:116. DOI: 10.3389/fpsyt.2017.00116
- Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. General Hospital Psychiatry Volume 35, Issue 4, July–August 2013, Pages 332-338. DOI: 10.1016/j.genhosppsych.2013.03.008
- License(s) stated are only a summary. Some software packages may use libraries under different licenses.
- AWARE: mobile context instrumentation framework; Ferreira D, Kostakos V and Dey AK Front. ICT (2015); 2:6; DOI:10.3389/fict.2015.00006
- 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):e16 DOI:10.2196/mental.5165
- Onnela lab's website. Retrieved June 23, 2017
- 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, Heidelberg DOI: 10.1007/978-3-540-75696-5_1.
- Smartphone Sensing Group at Darmouth's website. Retrieved June 30, 2017.
- 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. 
- 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; DOI: 10.1145/2077546.2077552
- "Purdue related startup creates experience capturing platform that could replace, improve traditional survey tools", Purdue Newsletter 4th quarter (2016). Retrieved June 25, 2017
- Happier People Live More Active Lives: Using Smartphones to Link Happiness and Physical Activity; Neal Lathia, Gillian M. Sandstrom, Cecilia Mascolo, Peter J. Rentfrow; (2017); PLoS ONE 12(1): e0160589; DOI: 10.1371/journal.pone.0160589
- emotionsense.github.io/data.html. Retrieved July 4, 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); DOI:10.1016/j.pmcj.2011.09.004
- funf.org/journal.html. Retrieved July 4, 2017.
- "mindLAMP Platform".
- Torous, John; Wisniewski, Hannah; Bird, Bruce; Carpenter, Elizabeth; David, Gary; Elejalde, Eduardo; Fulford, Dan; Guimond, Synthia; Hays, Ryan; Henson, Philip; Hoffman, Liza (2019-06-01). "Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach". Journal of Technology in Behavioral Science. 4 (2): 73–85. doi:10.1007/s41347-019-00095-w. ISSN 2366-5963. S2CID 150589575.
- 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):e75 DOI:10.2196/jmir.6678
- Personal communication.
- "Murmuras website".
- tech.cbits.northwestern.edu/2013/10/04/purple-robot-importer-purple-robot-warehouse. Retrieved June 30, 2017.
- PassiveDataKit website. Retrieved June 25, 2017
- 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);173:136-8 DOI:10.3233/978-1-61499-022-2-136
- Purple: A Modular System for Developing and Deploying Behavioral Intervention Technologies; Schueller SM, Begale M, Penedo FJ, Mohr DC; J Med Internet Res 2014;16(7):e181; DOI:10.2196/jmir.3376
- ResearchKit documentation. Retrieved June 23, 2017
- Apple’s ResearchKit: smart data collection for the smartphone era?; Jennifer Jardine, Jonathan Fisher, Benjamin Carrick; Journal of the Royal Society of Medicine; Vol 108, Issue 8, pp. 294 - 296 (2015); DOI:10.1177/0141076815600673.
- developer.sagebridge.org. Retrieved July 2, 2017.
- “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. Retrieved June 23, 2017
- 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; DOI: 10.1145/2639108.2642910
- Awareframework tutorials. Retrieved June 26, 2017.
- awareframework.com/sensors. Retrieved July 1, 2017.
- Beiwe research manuals provided by investigator.
- 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 at software.intel.com/en-us/documentation/context-sensing-sdk-for-android-states-datasheet for Android and at software.intel.com/en-us/documentation/context-sensing-sdk-for-windows-states-datasheet for Windows mobile. Retrieved July 1, 2017.
- The empath platform's sensors are external to the phone.
- emotionsense.github.io/sensors.html. Retrieved July 1, 2017.
- ilumivu.com/solutions/ecological-momentary-assessment-app/in-phone-sensors. Retrieved July 1, 2017.
- movisens.com/en/products/movisensxs. Retrieved July 2, 2017.
- www.psyt.co.uk/research. Retrieved July 2, 2017.
- Email communication, Katie Brown, Psychological Technologies (Psyt Ltd).
- tech.cbits.northwestern.edu/purple-robot. Retrieved June 27, 2017.
- www.lifedatacorp.com/features-reallife-exp-mobile-app. Retrieved July 2, 2017.
- 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 at researchkit.org/docs/docs/ActiveTasks/ActiveTasks.html); and (2) through the iOS HealthKit and CoreMotion (https://developer.apple.com/documentation/coremotion developer.apple.com/documentation/coremotion) APIs.
- Researchstack developers forum at Google groups. Retrieved July 2, 2017.
- sensingkit.org. Retrieved July 2, 2017.
- awareframework.com/context. Retrieved July 4, 2017.
- awareframework.com/run-a-study-with-aware. Retrieved July 4, 2017.
- "Beiwe Wiki". wiki.beiwe.org. Retrieved 2018-01-19.
- . Retrieved January 19, 2018
- funf.org/about.html. Retrieved July 4, 2017.
- www.awareframework.com/locations. Retrieved July 4, 2017.
- Speakersense: Energy efficient unobtrusive speaker identification on Mobile Phones; in Pervasive computing: 9th International Conference, Pervasive (2011) San Francisco; Edited by K Lyons, J Hightower, EM Huang
- Purplerobot blog entry about battery management. Retrieved July 4, 2017.
- awareframework.slack.com. Retrieved June 24, 2017
- . Retrieved January 19, 2018
- Context Sensing SDK online documentation. Retrieved June 29, 2017.
- Context sensing SDK Developers Forum. Retrieved June 29, 2017.
- Context Sensing SDK download page. Retrieved June 29, 2017.
- emotionsense.github.io. Retrieved July 3, 2017.
- Emotion Sense Developer's forum at Google Groups. Retrieved June 30, 2017.
- Elevate Ventures List of Awardees 2017. Retrieved July 3, 2017.
- Funf developers forum at Google groups. Retrieved June 26, 2107
- MovisensXS documentation. Retrieved June 26, 2017
- "MovisensXS Archives - movisens GmbH".
- passivedatakit.org/docs. Retrieved July 3, 2017.
- lifedata.zendesk.com/hc/en-us. Retrieved July 4, 2017.
- researchkit.org. Retrieved July 3, 2017.
- forums.developer.apple.com/community/researchkit. Retrieved July 3, 2017.
- http://researchstack.org/documentation. Retrieved July 2, 2017.
- Researchstack webinar on youtube. Retrieved July 2, 2017.
- awareframework.com/encrypting-a-studys-database. Retrieved July 4, 2017.
- Mobile Sensing Platform eMethods document (PDF). Retrieved July 4, 2017.
- "Certificate and Public Key Pinning".