1932

Abstract

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.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-clinpsy-032816-044949
2017-05-08
2024-06-14
Loading full text...

Full text loading...

/deliver/fulltext/clinpsy/13/1/annurev-clinpsy-032816-044949.html?itemId=/content/journals/10.1146/annurev-clinpsy-032816-044949&mimeType=html&fmt=ahah

Literature Cited

  1. Abdullah S, Matthews M, Murnane EL, Gay G, Choudhury T. 2014. Towards circadian computing: “Early to bed and early to rise” makes some of us unhealthy and sleep deprived. Proc. UbiComp ’14: 2014 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Seattle, WA673–84 New York: Assoc. Comput. Mach. [Google Scholar]
  2. Acampora G, Cook DJ, Rashidi P, Vasilakos AV. 2013. A survey on ambient intelligence in healthcare. Proc. IEEE 101:2470–94 [Google Scholar]
  3. Adams P, Rabbi M, Rahman T, Matthews M, Voida A. et al. 2014. Towards personal stress informatics: comparing minimally invasive techniques for measuring daily stress in the wild. Pervasive Health ’14: Proc. 8th Int. Conf. Pervasive Comput. Technol. Healthc., Oldenburg, Ger Brussels: Inst. Comput. Sci. Social-Inform. Telecom. Eng http://eudl.eu/doi/10.4108/icst.pervasivehealth.2014.254959 [Google Scholar]
  4. Alvarez-Lozano J, Osmani V, Mayora O, Frost M. 2014. Tell me your apps and I will tell you your mood: correlation of apps usage with Bipolar Disorder State Presented at Int. Conf. Pervasive Technol. Relat. Assist. Environ., 7th, Rhodes, Greece [Google Scholar]
  5. Andrews S, Ellis DA, Shaw H, Piwek L. 2015. Beyond self-report: tools to compare estimated and real-world smartphone use. PLOS ONE 10:e0139004 [Google Scholar]
  6. Asselbergs J, Ruwaard J, Ejdys M, Schrader N, Sijbrandij M, Riper H. 2016. Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study. J. Med. Internet Res 18e72 [Google Scholar]
  7. Ben-Zeev D, Wang R, Abdullah S, Brian R, Scherer EA. et al. 2016. Mobile behavioral sensing for outpatients and inpatients with schizophrenia. Psychiatr. Serv. 67:558–61 [Google Scholar]
  8. Bengio Y, Courville A, Vincent P. 2013. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35:1798–828 [Google Scholar]
  9. Berke EM, Choudhury T, Ali S, Rabbi M. 2011. Objective measurement of sociability and activity: mobile sensing in the community. Ann. Fam. Med. 9:344–50 [Google Scholar]
  10. Bishop CM. 2006. Pattern Recognition and Machine Learning New York: Springer [Google Scholar]
  11. Bishop CM, Lasserre J. 2007. Generative or discriminative? Getting the best of both worlds. Bayesian Stat 8:3–23 [Google Scholar]
  12. Brush AJB, Krumm J, Scott C. 2010. Exploring end user preferences for location obfuscation, location-based services, and the value of location. Proc. UbiComp ’10: 2010 ACM Conf. Ubiquitous Comput., Copenhagen, Denmark95–104 New York: Assoc. Comput. Mach. [Google Scholar]
  13. Burns MN, Begale M, Duffecy J, Gergle D, Karr CJ. et al. 2011. Harnessing context sensing to develop a mobile intervention for depression. J. Med. Internet Res 13e55 [Google Scholar]
  14. Butler D. 2013. When Google got flu wrong. Nature 494:155–56 [Google Scholar]
  15. Calvo RA, D'Mello S. 2010. Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1:18–37 [Google Scholar]
  16. Canzian L, Musolesi M. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. Proc. UbiComp ’15: 2015 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Osaka, Japan1293–304 New York: Assoc. Comput. Mach. [Google Scholar]
  17. Chapelle O, Schölkopf B, Zien A. 2006. Semi-Supervised Learning Cambridge, MA: MIT Press [Google Scholar]
  18. Chen ZY, Lin M, Chen FL, Lane ND, Cardone G. et al. 2013. Unobtrusive sleep monitoring using smartphones. Pervasive Health ’13: Proc. 7th Int. Conf. Pervasive Comput. Technol. Healthc., Venice, Italy145–52 Washington, DC: IEEE [Google Scholar]
  19. Choudhury T, Consolvo S, Harrison B, LaMarca A, LeGrand L. et al. 2008. The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput 7:32–41 [Google Scholar]
  20. Ciman M, Wac K, Gaggi O. 2015. Assessing stress through human-smartphone interaction analysis. Pervasive Health ’15: Proc. 9th Int. Conf. Pervasive Comput. Technol. Healthc., Istanbul. Brussels: Inst. Comput. Sci. Social-Inform. Telecom. Eng http://ieeexplore.ieee.org/document/7349382/ [Google Scholar]
  21. Collins FS, Varmus H. 2015. A new initiative on precision medicine. N. Engl. J. Med. 372:793–95 [Google Scholar]
  22. De Choudhury M, Counts S, Horvitz E. 2013a. Social media as a measurement tool of depression in populations. WebSci ’13: Proc. 5th Annu. ACM Web Sci. Conf., Paris47–56 New York: Assoc. Comput. Mach. [Google Scholar]
  23. De Choudhury M, Gamon M, Counts S, Horvitz E. 2013b. Predicting depression via social media. Proc. 7th. Int. AAAI Conf. Weblogs Social Media, Boston128–37 Palo Alto, CA: Assoc. Adv. Artif. Intell. [Google Scholar]
  24. De Choudhury M, Kiciman E, Dredze M, Coppersmith G, Kumar M. 2016. Discovering shifts to suicidal ideation from mental health content in social media. CHI’16: Proc. 2016 CHI Conf. Human Factors Comput. Systems, San Jose, CA2098–110 New York: Assoc. Comput. Mach. [Google Scholar]
  25. de Montjoye Y-A, Hidalgo CA, Verleysen M, Blondel VD. 2013. Unique in the crowd: the privacy bounds of human mobility. Sci. Rep. 3:1376 [Google Scholar]
  26. Eadicicco L. 2015. Americans check their phones 8 billion times a day. Time Dec. 15. http://time.com/4147614/smartphone-usage-us-2015/ [Google Scholar]
  27. Eagle N, Pentland A. 2006. Reality mining: sensing complex social systems. Pers. Ubiquitous Comput. 10:255–68 [Google Scholar]
  28. Eagle N, Pentland A, Lazer D. 2009. Inferring friendship network structure by using mobile phone data. PNAS 106:15274–78 [Google Scholar]
  29. Edison T, Essa I, Abowd G. 2015. A practical approach for recognizing eating moments with wrist-mounted inertial sensing. Proc. UbiComp ’15: 2015 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Osaka, Jpn.1029–40 New York: Assoc. Comput. Mach. [Google Scholar]
  30. Epp C, Lippold M, Mandryk RL. 2011. Identifying emotional states using keystroke dynamics. CHI’11: Proc. SIGCHI Conf. Hum. Factors Comput. Sys., Vancouver, Can715–24 New York: Assoc. Comput. Mach. [Google Scholar]
  31. Fortier I, Doiron D, Burton P, Raina P. 2011. Consolidating data harmonization—how to obtain quality and applicability?. Am. J. Epidemiol. 174:261–64; author reply 65–66 [Google Scholar]
  32. Gravenhorst F, Muaremi A, Bardram J, Grünerbl A, Mayora O. et al. 2015. Mobile phones as medical devices in mental disorder treatment: an overview. J. Pers. Ubiquitous Comput. 19:335–53 [Google Scholar]
  33. Grünerbl A, Muaremi A, Osmani V, Bahle G, Ohler S. et al. 2015. Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J. Biomed. Health Inform 19:140–48 [Google Scholar]
  34. Grünerbl A, Osmani V, Bahle G, Carrasco JC, Oehler S. et al. 2014. Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. AH’14: Proc. 5th Augment. Hum. Int. Conf., Kobe, Jpn., Mar. 7–9 Artic. 38 New York: Assoc. Comput. Mach. [Google Scholar]
  35. Gu W, Yang Z, Shangguan L, Sun W, Jin K, Liu Y. 2014. Intelligent sleep stage mining service with smartphones. Proc. UbiComp ’14: 2014 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Seattle, WA649–60 New York: Assoc. Comput. Mach. [Google Scholar]
  36. Hames JL, Hagan CR, Joiner TE. 2013. Interpersonal processes in depression. Annu. Rev. Clin. Psychol. 9:355–77 [Google Scholar]
  37. Hao T, Xing G, Zhou G. 2013. iSleep: unobtrusive sleep quality monitoring using smartphones. SenSys’13: Proc. 11th ACM Conf. Embed. Netw. Sens. Syst., Rome Artic. 4 New York: Assoc. Comput. Mach. [Google Scholar]
  38. Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR. et al. 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Proc. Mag. 29:82–97 [Google Scholar]
  39. Hsieh H-P, Li C-T. 2014. Inferring social relationships from mobile sensor data. WWW ’14 Companion: Proc. 23rd Int. Conf. World Wide Web, Seoul, Korea293–94 New York: Assoc. Comput. Mach. [Google Scholar]
  40. Intille SS. 2013. Closing the evaluation gap in UbiHealth Research. IEEE Pervasive Comput 12:76–79 [Google Scholar]
  41. Jain SH, Powers BW, Hawkins JB, Brownstein JS. 2015. The digital phenotype. Nat. Biotechnol. 33:462–63 [Google Scholar]
  42. Jashinsky J, Burton SH, Hanson CL, West J, Giraud-Carrier C. et al. 2014. Tracking suicide risk factors through Twitter in the US. Crisis 35:51–59 [Google Scholar]
  43. Kalantarian H, Alshurafa N, Le T, Sarrafzadeh M. 2015. Monitoring eating habits using a piezoelectric sensor-based necklace. Comput. Biol. Med. 58:46–55 [Google Scholar]
  44. Kay M, Patel SN, Kientz JA. 2015. How good is 85%? A survey tool to connect classifier evaluation to acceptability of accuracy. CHI’15: Proc. 2016 CHI Conf. Human Factors Comput. Syst., Seoul, Korea347–56 New York: Assoc. Comput. Mach. [Google Scholar]
  45. Klasnja P, Consolvo S, McDonald DW, Landay JA, Pratt W. 2009. Using mobile & personal sensing technologies to support health behavior change in everyday life: lessons learned. AMIA Annu. Symp. Proc. 2009338–42 [Google Scholar]
  46. Krizhevsky A, Sutskever I, Hinton GE. 2012. ImageNet classification with deep convolutional neural networks. Proc. 25th Int. Conf. Neural Inf. Process. Syst., Lake Tahoe, NV1097–105 Red Hook, NY: Curran Assoc. [Google Scholar]
  47. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44 [Google Scholar]
  48. Li I, Dey A, Forlizzi J. 2010. A stage-based model of personal informatics systems. CHI’10: Proc. 2016 CHI Conf. Human Factors Comput. Syst., Atlanta557–66 New York: Assoc. Comput. Mach. [Google Scholar]
  49. Li Q, Stankovic JA, Hanson MA, Barth AT, Lach J, Zhou G. 2009. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. Proc. Sixth Int. Workshop Wearable Implant. Body Sens. Netw.138–43 Washington, DC: IEEE [Google Scholar]
  50. LiKamWa R, Liu Y, Lane ND, Zhong L. 2013. MoodScope: building a mood sensor from smartphone usage patterns. MobiSys ’13: Proc. 11th Annu. Int. Conf. Mob. Syst. Appl. Serv., Taipei, Taiwan389–402 New York: Assoc. Comput. Mach. [Google Scholar]
  51. Lim BY, Dey AK. 2011. Investigating intelligibility for uncertain context-aware applications. Proc. UbiComp ’11: 2014 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Beijing415–24 New York: Assoc. Comput. Mach. [Google Scholar]
  52. Lu H, Rabbi M, Chittaranjan GT, Frauendorfer D, Mast MS. et al. 2012. Detecting stress in unconstrained acoustic environments using smartphones. Proc. UbiComp ’12: 2014 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Pittsburgh, PA351–60 New York: Assoc. Comput. Mach. [Google Scholar]
  53. Ma Y, Xu B, Bai Y, Sun G, Zhu H. 2012. Daily mood assessment based on mobile phone sensing. Proc. 2012 9th Int. Conference Wearable Implant. Body Sens. Netw., London142–47 Washington, DC: IEEE [Google Scholar]
  54. Madan A, Cebrian M, Lazer D, Pentland A. 2010. Social sensing for epidemiological behavior change. Proc. UbiComp ’10: 2010 ACM Conf. Ubiquitous Comput., Copenhagen, Den291–300 New York: Assoc. Comput. Mach [Google Scholar]
  55. Maehr W. 2008. eMotion: Estimation of User's Emotional State by Mouse Motions Saarbrücken, Ger.: VDM [Google Scholar]
  56. Mayora O, Arnrich B, Bardram J, Drager C, Finke A. et al. 2013. Personal health systems for bipolar disorder: anecdotes, challenges and lessons learnt from MONARCA Project. Pervasive Health ’13: Proc. 7th Int. Conf. Pervasive Comput. Technol. Healthc., Venice, Italy424–29 Washington, DC: IEEE [Google Scholar]
  57. 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.477–86 New York: Assoc. Comput. Mach. [Google Scholar]
  58. Min J-M, Wiese J, Hong J, Zimmerman J. 2013. Mining smartphone data to classify life-facets of social relationships. CSCW’13: Proc. 2013 Conf. Comput.-Support. Coop. Work Soc. Comput., San Antonio, TX285–94 New York: Assoc. Comput. Mach. [Google Scholar]
  59. Miotto R, Li L, Kidd BA, Dudley JT. 2016. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6:26094 [Google Scholar]
  60. 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.843–54 New York: Assoc. Comput. Mach. [Google Scholar]
  61. 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.841–48 Cambridge, MA: MIT Press [Google Scholar]
  62. Oxman TE, Rosenberg SD, Tucker GJ. 1982. The language of paranoia. Am. J. Psychiatry 139:275–82 [Google Scholar]
  63. Palmius N, Tsanas A, Saunders K, Bilderbeck AC, Geddes JR. et al. 2016. Detecting bipolar depression from geographic location data. IEEE Trans. Biomed. Eng99 https://doi.org/ 10.1109/TBME.2016.2611862 [Google Scholar]
  64. Park G, Schwartz HA, Eichstaedt JC, Kern ML, Kosinski M. et al. 2015. Automatic personality assessment through social media language. J. Pers. Soc. Psychol. 108:934–52 [Google Scholar]
  65. Perrin A. 2015. Social media usage: 2005–2015 Rep., Pew Research Center, Washington, DC. http://www.pewinternet.org/2015/10/08/social-networking-usage-2005-2015/ [Google Scholar]
  66. 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 York265–76 New York: Assoc. Comput. Mach. [Google Scholar]
  67. Picard RW, Fedor S, Ayzenberg Y. 2016. Multiple arousal theory and daily-life electrodermal activity asymmetry. Emot. Rev. 8:62–75 [Google Scholar]
  68. Piwek L, Ellis DA, Andrews S, Joinson A. 2016. The rise of consumer health wearables: promises and barriers. PLOS Med 13:e1001953 [Google Scholar]
  69. Poushter J. 2016. Smartphone ownership and Internet usage continues to climb in emerging economies Rep., Pew Research Center, Washington, DC. http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/ [Google Scholar]
  70. 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, Denmark281–90 New York: Assoc. Comput. Mach. [Google Scholar]
  71. Rahman T, Adams AT, Zhang M, Cherry E, Zhou B. et al. 2014. BodyBeat: a mobile system for sensing non-speech body sounds. MobiSys ’14: Proc. 12th Annu. Int. Conf. Mob. Syst. Appl. Serv., Bretton Woods, NH2–13 New York: Assoc. Comput. Mach. [Google Scholar]
  72. Ricker T. 2015. Wearables are booming but is anyone still wearing them?. The Verge Dec. 4. http://www.theverge.com/2015/12/4/9848394/wearable-abandonment [Google Scholar]
  73. Rude SS, Gortner EM, Pennebaker JW. 2004. Language use of depressed and depression-vulnerable college students. Cogn. Emot. 18:1121–33 [Google Scholar]
  74. Saeb S, Lattie E, Schueller SM, Kording K, Mohr DC. 2016. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4:e2537 [Google Scholar]
  75. Saeb S, Lonini L, Kording K, Mohr DC. 2016. Voodoo machine learning for clinical predictions. bioRxiv 059774. https://doi.org/10.1101/059774
  76. Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME. et al. 2015. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J. Med. Internet Res 17e175 [Google Scholar]
  77. Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Netw 61:85–117 [Google Scholar]
  78. Schwartz HA, Eichstaedt J, Kern M, Park G, Sap M. et al. 2014. Towards assessing changes in degree of depression through Facebook. Proc. Workshop Comput. Linguist. Clin. Psychol. Linguist. Signal Clin. Real., Baltimore, MD118–25 Stroudsburg, PA: Assoc. Comput. Linguist. [Google Scholar]
  79. Sen S, Subbaraju V, Misra A, Balan RK, Lee Y. 2015. The case for smartwatch-based diet monitoring. Proc. 2015 IEEE Int. Conf. Pervasive Comput. Commun. Workshops (PerCom Workshops), St. Louis, MO.585–90 Washington, DC: IEEE [Google Scholar]
  80. Settles B. 2010. Active learning literature survey Comput. Sci. Tech. Rep. 1648, Univ. Wisconsin, Madison. http://burrsettles.com/pub/settles.activelearning.pdf [Google Scholar]
  81. Shilton K. 2009. Four billion little brothers? Privacy, mobile phones, and ubiquitous data collection. Commun. ACM 52:48–53 [Google Scholar]
  82. Shilton K, Sayles S. 2016. “We aren't all going to be on the same page about ethics”: ethical practices and challenges in research on digital and social media. Proc. 2016 49th Hawaii Int. Conf. Syst. Sci., Honolulu, HI1909–18 Washington, DC: IEEE [Google Scholar]
  83. Sivertsen B, Krokstad S, Overland S, Mykletun A. 2009. The epidemiology of insomnia: associations with physical and mental health. The HUNT-2 study. J. Psychosom. Res. 67:109–16 [Google Scholar]
  84. Spenkelink CD, Hutten MM, Hermens HJ, Greitemann BO. 2002. Assessment of activities of daily living with an ambulatory monitoring system: a comparative study in patients with chronic low back pain and nonsymptomatic controls. Clin. Rehabil. 16:16–26 [Google Scholar]
  85. Sun D, Paredes P, Canny J. 2014. MouStress: detecting stress from mouse motion. CHI’14: Proc. SIGCHI Conf. Human Factors Comput. Syst., Toronto, Can.61–70 New York: Assoc. Comput. Mach. [Google Scholar]
  86. 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, Can3104–12 Cambridge, MA: MIT Press [Google Scholar]
  87. Sweeney L. 2000. Simple demographics often identify people uniquely Data Priv. Work. Pap. 3, Carnegie Mellon Univ., Pittsburgh, PA [Google Scholar]
  88. Taylor DJ, Lichstein KL, Durrence HH, Reidel BW, Bush AJ. 2005. Epidemiology of insomnia, depression, and anxiety. Sleep 28:1457–64 [Google Scholar]
  89. Torous J, Kiang MV, Lorme J, Onnela JP. 2016. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Mental Health 3:e16 [Google Scholar]
  90. Vallance JK, Winkler EA, Gardiner PA, Healy GN, Lynch BM, Owen N. 2011. Associations of objectively-assessed physical activity and sedentary time with depression: NHANES (2005–2006). Prev. Med. 53:284–88 [Google Scholar]
  91. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA. 2010. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11:3371–408 [Google Scholar]
  92. Wang D, Pedreschi D, Song C, Giannotti F, Barabási A-L. 2011. Human mobility, social ties and link prediction. KDD ’17: Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., San Diego, CA1100–8 New York: Assoc. Comput. Mach. [Google Scholar]
  93. Wang R, Chen FL, Chen Z, Li TX, Farari G. et al. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. Proc. UbiComp ’14: 2014 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Seattle, WA3–14 New York: Assoc. Comput. Mach. [Google Scholar]
  94. Wang R, Aung MSH, Abdullah S, Brian R, Campbell AT. et al. 2016. CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia. Proc. UbiComp ’16: 2016 ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Heidelberg, Ger.886–97 New York: Assoc. Comput. Mach. [Google Scholar]
  95. 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. 13–17643–647 New York: Assoc. Comput. Mach. [Google Scholar]
  96. Wiese J, Min J-M, Hong J, Zimmerman J. 2015. “You never call, you never write”: call and SMS logs do not always indicate tie strength. CSCW’15: Proc. 18th ACM Conf. Comput. Support. Coop. Work Soc. Comput., Vancouver, Can.765–74 New York: Assoc. Comput. Mach. [Google Scholar]
  97. Wiskott L, Sejnowski TJ. 2002. Slow feature analysis: unsupervised learning of invariances. Neural Comput 14:715–70 [Google Scholar]
  98. Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D. et al. 2015. The human splicing code reveals new insights into the genetic determinants of disease. Science 347:1254806 [Google Scholar]
  99. Yatani K, Truong KN. 2012. BodyScope: a wearable acoustic sensor for activity recognition. Proc. UbiComp ’12: 2012 ACM Conf. Ubiquitous Comput., Pittsburgh, PA341–50 New York: Assoc. Comput. Mach. [Google Scholar]
  100. 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, Italy145–52 Washington, DC: IEEE [Google Scholar]
  101. Zhu X, Goldberg AB. 2009. Introduction to Semi-Supervised Learning San Rafael, CA: Morgan & Claypool [Google Scholar]
/content/journals/10.1146/annurev-clinpsy-032816-044949
Loading
/content/journals/10.1146/annurev-clinpsy-032816-044949
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error