Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Open accessAbstract
Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.
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Multimodal and intensive sampling data streams might be particularly promising inputs for machine learning models because these models can often handle many correlated predictors.
“Multimodal and intensive sampling data streams (for example, experience sampling and passive measures gathered through smartphones and other electronic devices) used in some MBI studies133 and mental health research broadly134,135 might be particularly promising inputs for machine learning models, given that these models can often handle many correlated predictors136.”
From A common factors perspective on mindfulness-based interventions (2022) · cites this paper as [133]
Close interrelationships between everyday behavior, physiology, and mental well-being make digital phenotyping with wearables attractive for detection of mental disorders.
“Close interrelationships between everyday behavior, physiology, and mental well-being makes digital phenotyping with wearables especially attractive for detection of mental disorders and discovering respective digital biomarkers [8-11].”
From Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling (2021) · cites this paper as [9]
Smartphone embedded sensors make it possible to indirectly collect data about patients' behaviours and habits, such as social media use, physical activity, or social interactions.
“Unobtrusive wearable biosensors can continuously monitor physiological parameters throughout the day with high precision [17], whereas smartphone embedded sensors make it possible to indirectly collect data about patients’ behaviours and habits, such as their social media use, physical activity, or social interactions [18,19].”
From Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review (2019) · cites this paper as [18]
The hierarchical sensing model proposed by Mohr highlights the revolution that new sensors and biosensors can bring to mental health, making it possible to collect raw sensor data that can be converted into behavioural markers through machine learning and data mining.
“the hierarchical sensing model proposed by Mohr highlights the great revolution that new sensors and biosensors can bring to the field of mental health [85], making it possible to collect raw sensor data (i.e., the lower level of the hierarchy) that can be converted into “behavioural markers” through machine learning and data mining methods [18]”
From Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review (2019) · cites this paper as [18]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202 · see citing works
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791 · see citing works
Chapelle, O., Schölkopf, B., & Zien, A. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.001.0001 · Cited by 4,560 · see citing works
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works
Pattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,782 · see citing works
Reality mining: sensing complex social systems
Eagle, N., & (Sandy) Pentland, A. (2006) · Personal and Ubiquitous Computing · doi:10.1007/s00779-005-0046-3 · Cited by 2,772 · see citing works
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · cited 1× · see citing works
“Such images can be mined to determine the environmental factors that affect mental health and well-being (e.g., the amount of green space / 11 17 . number of trees in a neighborhood on rapidly expanding area is ambient intelligence, in which sensors are installed on everyday objects and in places…”
BackgroundInferring friendship network structure by using mobile phone data
Eagle, N., Pentland, A. (., & Lazer, D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0900282106 · Cited by 1,856 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
Unique in the Crowd: The privacy bounds of human mobility
de Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013) · Scientific Reports · doi:10.1038/srep01376 · Cited by 1,645 · see citing works
Slow Feature Analysis: Unsupervised Learning of Invariances
Wiskott, L., & B, T. J. S. A. (2002) · Neural Computation · doi:10.1162/089976602317318938 · Cited by 1,351 · see citing works
The Rise of Consumer Health Wearables: Promises and Barriers
Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1001953 · Cited by 1,301 · see citing works
The human splicing code reveals new insights into the genetic determinants of disease
Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K. C., Hua, Y., Gueroussov, S., Najafabadi, H. S., Hughes, T., et al. (2014) · Science · doi:10.1126/science.1254806 · Cited by 1,225 · see citing works
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2632048.2632054 · Cited by 1,116 · see citing works
A stage-based model of personal informatics systems
Li, I., Dey, A., & Forlizzi, J. (2010) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753409 · Cited by 1,114 · see citing works
Introduction to Semi-Supervised Learning
Zhu, X., & Goldberg, A. B. (2009) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00196ed1v01y200906aim006 · Cited by 1,025 · see citing works
Language use of depressed and depression-vulnerable college students
Rude, S., Gortner, E.-M., & Pennebaker, J. (2004) · Cognition & Emotion · doi:10.1080/02699930441000030 · Cited by 1,018 · see citing works
Epidemiology of Insomnia, Depression, and Anxiety
Taylor, D. J., Lichstein, K. L., Durrence, H. H., Reidel, B. W., & Bush, A. J. (2005) · Sleep · doi:10.1093/sleep/28.11.1457 · Cited by 988 · see citing works
Automatic personality assessment through social media language.
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L. H., & Seligman, M. E. P. (2015) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000020 · Cited by 802 · see citing works
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4273 · Cited by 782 · see citing works
Self-citationTorous, J., Kiang, M. V., Lorme, J., & Onnela, J.-P. (2016) · JMIR Mental Health · doi:10.2196/mental.5165 · Cited by 758 · cited 1× · see citing works
“a be as reality mining (Eagle & Pentland 2006), personal informatics (Li et al. 2010), digital phenotyping (Jain et al. 2015, Torous et al. 2016), and personal sensing (Klasnja et al. 2009).”
BackgroundDiscovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
Choudhury, M. D., Kıcıman, E., Dredze, M. H., Coppersmith, G., & Kumar, M. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858207 · Cited by 682 · see citing works
Human mobility, social ties, and link prediction
Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabási, A.-Ĺ. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020581 · Cited by 671 · see citing works
Simple Demographics Often Identify People Uniquely
Sweeney, L. (2000) · Figshare · doi:10.1184/r1/6625769.v1 · Cited by 664 · see citing works
A Survey on Ambient Intelligence in Healthcare
Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2262913 · Cited by 655 · see citing works
Harnessing context sensing to develop a mobile intervention for depression
Burns, M. N., Begale, M., Duffecy, J., Gergle, D., Karr, C. J., Giangrande, E., & Mohr, D. C. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1838 · Cited by 646 · see citing works
Self-citationThe Mobile Sensing Platform: An Embedded Activity Recognition System
Choudhury, T., Borriello, G., Consolvo, S., Haehnel, D., Harrison, B. L., Hemingway, B., Hightower, J. R., Klasnja, P., Koscher, K., LaMarca, A., et al. (2008) · IEEE Pervasive Computing · doi:10.1109/mprv.2008.39 · Cited by 598 · see citing works
Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information
Li, Q., Stankovic, J. A., Hanson, M., Barth, A. T., Lach, J., & Zhou, G. (2009) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2009.46 · Cited by 588 · see citing works
Butler, D. (2013) · Nature · doi:10.1038/494155a · Cited by 517 · see citing works
Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., Gática-Pérez, D., & Choudhury, T. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370270 · Cited by 511 · see citing works
Interpersonal Processes in Depression
Hames, J. L., Hagan, C. R., & Joiner, T. E. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185553 · Cited by 499 · see citing works
Social media as a measurement tool of depression in populations
De Choudhury, M., Counts, S., & Horvitz, E. (2013) · Proceedings of the 5th Annual ACM Web Science Conference · doi:10.1145/2464464.2464480 · Cited by 497 · see citing works
Canzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477 · see citing works
MoodScope: building a mood sensor from smartphone usage patterns
LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013) · Proceeding of the 11th annual international conference on Mobile systems, applications, and services · doi:10.1145/2462456.2464449 · Cited by 444 · see citing works
Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015) · Nature Biotechnology · doi:10.1038/nbt.3223 · Cited by 425 · see citing works
Beyond self-report: tools to compare estimated and real-world smartphone use
Andrews, S., Ellis, D. A., Shaw, H., & Piwek, L. (2015) · PLOS ONE · doi:10.1371/journal.pone.0139004 · Cited by 408 · see citing works
Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients
Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., & Lukowicz, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2343154 · Cited by 384 · see citing works
The need to approximate the use-case in clinical machine learning
Saeb, S., Lonini, L., Jayaraman, A., Mohr, D. C., & Kording, K. P. (2017) · GigaScience · doi:10.1093/gigascience/gix019 · Cited by 367 · see citing works
Self-citationIdentifying emotional states using keystroke dynamics
Epp, C., Lippold, M., & Mandryk, R. L. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979046 · Cited by 364 · see citing works
Tracking suicide risk factors through Twitter in the US.
Jashinsky, J., Burton, S. H., Hanson, C., West, J. H., Giraud-Carrier, C., Barnes, M. D., & Argyle, T. (2014) · Crisis · doi:10.1027/0227-5910/a000234 · Cited by 363 · see citing works
The epidemiology of insomnia: Associations with physical and mental health.
Sivertsen, B., Krokstad, S., Øverland, S. N., & Mykletun, A. (2009) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2009.05.001 · Cited by 341 · see citing works
The relationship between mobile phone location sensor data and depressive symptom severity
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016) · PeerJ · doi:10.7717/peerj.2537 · Cited by 313 · see citing works
Self-citationBodyScope: a wearable acoustic sensor for activity recognition
Yatani, K., & Truong, K. N. (2012) · Proceedings of the 2012 ACM Conference on Ubiquitous Computing · doi:10.1145/2370216.2370269 · Cited by 310 · see citing works
Unobtrusive Sleep Monitoring using Smartphones
Chen, Z., Lin, M., Chen, F., Lane, N., Cardone, G., Wang, R., Li, T., Chen, Y., Choudhury, T., & Cambell, A. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/icst.pervasivehealth.2013.252148 · Cited by 300 · see citing works
A practical approach for recognizing eating moments with wrist-mounted inertial sensing
Thomaz, E., Essa, I. A., & Abowd, G. D. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2807545 · Cited by 300 · see citing works
Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry
Picard, R. W., Fedor, S., & Ayzenberg, Y. (2015) · Emotion Review · doi:10.1177/1754073914565517 · Cited by 288 · see citing works
Wang, R., Aung, M. S. H., Abdullah, S., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J., Merrill, M., Scherer, E. A., et al. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971740 · Cited by 257 · see citing works
Towards Assessing Changes in Degree of Depression through Facebook
Schwartz, H. A., Eichstaedt, J., Kern, M. L., Park, G., Sap, M., Stillwell, D., Kosinski, M., & Ungar, L. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3214 · Cited by 256 · see citing works
Vallance, J. K., Winkler, E. A. H., Gardiner, P. A., Healy, G. N., Lynch, B. M., & Owen, N. (2011) · Preventive Medicine · doi:10.1016/j.ypmed.2011.07.013 · Cited by 238 · see citing works
Shilton, K. (2009) · Communications of the ACM · doi:10.1145/1592761.1592778 · Cited by 220 · see citing works
Social sensing for epidemiological behavior change
Madan, A., Cebrian, M., Lazer, D., & Pentland, A. (2010) · Proceedings of the 12th ACM international conference on Ubiquitous computing · doi:10.1145/1864349.1864394 · Cited by 219 · see citing works
iSleep: unobtrusive sleep quality monitoring using smartphones
Hao, T., Xing, G., & Zhou, G. (2013) · Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2517351.2517359 · Cited by 216 · see citing works
Min, J.-K., Doryab, A., Wiese, J., Amini, S., Zimmerman, J., & Hong, J. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557220 · Cited by 197 · see citing works
Mobile phones as medical devices in mental disorder treatment: an overview
Gravenhorst, F., Muaremi, A., Bardram, J. E., Grünerbl, A., Mayora, O., Wurzer, G., Frost, M. M., Osmani, V., Bert Arnrich, A., Lukowicz, P., et al. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0829-5 · Cited by 196 · see citing works
Asselbergs, J., Ruwaard, J., Ejdys, M., Schrader, N., Sijbrandij, M., & Riper, H. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5505 · Cited by 192 · see citing works
Detecting Bipolar Depression From Geographic Location Data
Palmius, N., Tsanas, A., Saunders, K. E. A., Bilderbeck, A. C., Geddes, J. R., Goodwin, G. M., & De Vos, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2611862 · Cited by 180 · see citing works
Rahman, T., Adams, A. T., Zhang, M., Cherry, E., Zhou, B., Peng, H., & Choudhury, T. (2014) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2594368.2594386 · Cited by 159 · see citing works
Self-citationPham, H., Shahabi, C., & Liu, Y. (2013) · Proceedings - ACM-SIGMOD International Conference on Management of Data · doi:10.1145/2463676.2465301 · Cited by 152 · see citing works
Monitoring eating habits using a piezoelectric sensor-based necklace
Kalantarian, H., Alshurafa, N. I., Le, T., & Sarrafzadeh, M. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.01.005 · Cited by 152 · see citing works
Brush, A. J. B., Krumm, J. C., & Scott, J. (2010) · doi:10.1145/1864349.1864381 · Cited by 144 · see citing works
Spenkelink, C. D., Hutten, M. M. R., Hermens, H. J., & Greitemann, B. (2002) · Clinical Rehabilitation · doi:10.1191/0269215502cr463oa · Cited by 142 · see citing works
Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference · doi:10.1145/2582051.2582089 · Cited by 139 · see citing works
Abdullah, S., Matthews, M. L., Murnane, E. L., Gay, G. K., & Choudhury, T. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2632048.2632100 · Cited by 125 · see citing works
Mobile Behavioral Sensing for Outpatients and Inpatients With Schizophrenia
Ben‐Zeev, D., Wang, R., Abdullah, S., Brian, R., Scherer, E. A., Mistler, L. A., Hauser, M., Kane, J. M., Campbell, A. T., & Choudhury, T. (2015) · Psychiatric Services · doi:10.1176/appi.ps.201500130 · Cited by 125 · see citing works
Adams, P., Rabbi, M., Rahman, T., Matthews, M. L., Voida, A., Gay, G. K., Choudhury, T., & Voida, S. (2014) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2014.254959 · Cited by 113 · see citing works
Sun, D., Paredes, P. E., & Canny, J. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557243 · Cited by 110 · see citing works
Investigating intelligibility for uncertain context-aware applications
Lim, B. Y., & Dey, A. K. (2011) · Proceedings of the 13th international conference on Ubiquitous computing · doi:10.1145/2030112.2030168 · Cited by 101 · see citing works
Klasnja, P., Consolvo, S., McDonald, D. W., Landay, J. A., & Pratt, W. (2009) · PubMed Central · Cited by 100 · see citing works
The case for smartwatch-based diet monitoring
Sen, S., Subbaraju, V., Misra, A., Balan, R. K., & Lee, Y. (2015) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2015.7134103 · Cited by 89 · see citing works
Daily Mood Assessment Based on Mobile Phone Sensing
Ma, Y., Xu, B., Bai, Y. L., Sun, G., & Zhu, R. (2012) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2012.3 · Cited by 88 · see citing works
Invited Commentary: Consolidating Data Harmonization--How to Obtain Quality and Applicability?
Fortier, I., Doiron, D., Burton, P. R., & Raina, P. S. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr194 · Cited by 88 · see citing works
Mining smartphone data to classify life-facets of social relationships
Min, J.-K., Wiese, J., Hong, J., & Zimmerman, J. (2013) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2441776.2441810 · Cited by 81 · see citing works
Intelligent sleep stage mining service with smartphones
Gu, W., Yang, Z., Shangguan, L., Sun, W., Jin, K., & Liu, Y. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2632048.2632084 · Cited by 78 · see citing works
Tell me your apps and I will tell you your mood
Alvarez-Lozano, J., Osmani, V., Mayora, O., Frost, M. M., Bardram, J. E., Faurholt‐Jepsen, M., & Kessing, L. V. (2014) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2674396.2674408 · Cited by 71 · see citing works
Objective Measurement of Sociability and Activity: Mobile Sensing in the Community
Berke, E. M., Choudhury, T., Ali, S., & Rabbi, M. (2011) · The Annals of Family Medicine · doi:10.1370/afm.1266 · Cited by 66 · see citing works
(1982) · American Journal of Psychiatry · doi:10.1176/ajp.139.3.275 · Cited by 60 · see citing works
Kay, M., Patel, S., & Kientz, J. A. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702603 · Cited by 59 · see citing works
"You Never Call, You Never Write"
Wiese, J., Min, J.-K., Hong, J., & Zimmerman, J. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675143 · Cited by 55 · see citing works
iSensestress: Assessing stress through human-smartphone interaction analysis
Ciman, M., Wac, K., & Gaggi, O. (2015) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2015.259280 · Cited by 52 · see citing works
Shilton, K., & Sayles, S. (2016) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2016.242 · Cited by 44 · see citing works
Voodoo Machine Learning for Clinical PredictionsPreprint
Saeb, S., Lonini, L., Jayaraman, A., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/059774 · Cited by 44 · see citing works
Murnane, E. L., Abdullah, S., Matthews, M. L., Choudhury, T., & Gay, G. K. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2807522 · Cited by 41 · see citing works
eMotion: Estimation of User's Emotional State by Mouse Motions
Maehr, W. (2008) · Cited by 35 · see citing works
Mayora, O., Bert Arnrich, A., Bardram, J. E., Dräger, C., Finke, A., Frost, M. M., Giordano, S., Gravenhorst, F., Grünerbl, A., Häring, C., et al. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/icst.pervasivehealth.2013.252123 · Cited by 29 · see citing works
Inferring social relationships from mobile sensor data
Hsieh, H.-P., & Li, C. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2577365 · Cited by 14 · see citing works
Closing the Evaluation Gap in UbiHealth Research
Intille, S. S. (2013) · IEEE Pervasive Computing · doi:10.1109/mprv.2013.28 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 13
- De Choudhury M, Gamon M, Counts S, Horvitz E. 2013b Predicting depression via social media Proc. 7th.Int. AAAI Conf. Weblogs Social Media, Boston, pp. 128– 37. Palo Alto, CA: Assoc. Adv. Artif. Intell.
- Krizhevsky A, Sutskever I, Hinton GE. 2012 ImageNet classification with deep convolutional neural networks Proc. 25th Int. Conf. Neural Inf. Process. Syst., Lake Tahoe, NV, pp. 1097– 105. Red Hook, NY: Curran Assoc.
- Lu H, Rabbi M, Chittaranjan GT, Frauendorfer D, Mast MS, 2012 Detecting stress in unconstrained acoustic environments using smartphones Proc. UbiComp ‘12: 2014 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Pittsburgh, PA, pp. 351– 60. New York: Assoc. Comput. Mach.
- Min J-M, Doryab A, Wiese J, Amini S, Zimmerman J, Hong JI. 2014 Toss ‘n’ turn: smartphone as sleep and sleep quality detector CHI’10: Proc. 2016 CHI Conf. Human Factors Comput. Syst., Toronto, Can, pp. 477– 86. New York: Assoc. Comput. Mach.
- Murnane EL, Abdullah S, Matthews M, Choudhury T, Gay G. 2015 Social (media) jet lab: how usage of social technology can modulate and reflect circadian rhythms Proc. UbiComp ‘15: 2015 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Osaka, Jpn, pp. 843– 54. New York: Assoc. Comput. Mach.
- Ng AY, Jordan MI. 2002 On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes NIPS’01: Proc. 14th Int. Conf. Neural Inf. Process. Syst. Nat. Synth., Vancouver, Can, pp. 841– 48. Cambridge, MA: MIT Press
- Pham H, Shahabi C, Liu Y. 2013 EBM: an entropy-based model to infer social strength from spatiotemporal data SIGNMOD ‘13: Proc. 2013 ACM SIGMOD Int. Conf. Manag. Data, New York, pp. 265– 76. New York: Assoc. Comput. Mach.
- Rachuri KK, Musolesi M, Mascolo C, Rentfrow PJ, Longworth C, Aucinas A. 2010 Emotion sense: a mobile phones based adaptive platform for experimental social psychology research Proc. UbiComp ‘10: 2010 ACM Conf. Ubiquitous Comput., Copenhagen, Denmark, pp. 281– 90. New York: Assoc. Comput. Mach.
- Rahman T, Adams AT, Zhang M, Cherry E, Zhou B, 2014 BodyBeat: a mobile system for sensing non-speech body sounds MobiSys ‘14: Proc. 12th Annu. Int. Conf. Mob. Syst. Appl. Serv., Bretton Woods, NH, pp. 2– 13. New York: Assoc. Comput. Mach.
- Sun D, Paredes P, Canny J. 2014 MouStress: detecting stress from mouse motion CHI’14: Proc. SIGCHI Conf. Human Factors Comput. Syst., Toronto, Can, pp. 61– 70. New York: Assoc. Comput. Mach.
- Sutskever I, Vinyals O, Le QV. 2014 Sequence to sequence learning with neural networks NIPS’14: Proc. 27th Int. Conf. Neural Inf. Process. Syst., Montreal, Can, pp. 3104– 12. Cambridge, MA: MIT Press
- Wiese J, Hong JI, Zimmerman J. 2014 Challenges and opportunites in data mining contact lists for inferring relationships Proc. UbiComp ‘14: 2014 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Seattle, WA, Sept. 13–17, pp. 643– 647. New York: Assoc. Comput. Mach.
- Zhenyu C, Lane N, Cardone G, Lin M, Choudhury T, Campbell A. 2013 Unobtrusive sleep monitoring using smartphones Pervasive Health ‘13: Proc. 7th Int. Conf. Pervasive Comput. Technol. Healthc., Venice, Italy, pp. 145– 52. Washington, DC: IEEE
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- Authors
- Mohr, D. C., Zhang, M., & Schueller, S. M.
- Affiliations
- Northwestern University (US)
- Michigan State University (US)
- Venue
- Annual Review of Clinical Psychology
- Publisher
- Annual Reviews
- Published
- 2017-05-08
- Volume
- 13
- Issue
- 1
- Pages
- 23-47
- ISSN
- 1548-5943 (ISSN-L)
- 1548-5951
- PMC
- PMC6902121
- OpenAlex
- W2520441859
- Cited by
- 802
- References
- 90
- Citations by year
- 2026: 102 · 2025: 103 · 2024: 105 · 2023: 92 · 2022: 94 · 2021: 99 · 2020: 89 · 2019: 73 · 2018: 32 · 2017: 12 · 2016: 1
- FWCI
- 6.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Clinical Psychology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (P20MH090318, R01MH111610, R01MH100482, R01MH095753, K08MH102336)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- digital phenotyping, mental health, ubiquitous sensors, smartphones, wearable sensors, social media, digital traces, privacy, dimensionality, mobile health, mobile health intervention, mental health monitoring
- MeSH terms
- Machine Learning, Mental Disorders, Humans, Telemedicine, Neurophysiological Monitoring
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