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Passive and In-Situ assessment of mental and physical well-being using mobile sensors
Open accessAbstract
The idea of continuously monitoring well-being using mobile-sensing systems is gaining popularity. In-situ measurement of human behavior has the potential to overcome the short comings of gold-standard surveys that have been used for decades by the medical community. However, current sensing systems have mainly focused on tracking physical health; some have approximated aspects of mental health based on proximity measurements but have not been compared against medically accepted screening instruments. In this paper, we show the feasibility of a multi-modal mobile sensing system to simultaneously assess mental and physical health. By continuously capturing fine grained motion and privacy-sensitive audio data, we are able to derive different metrics that reflect the results of commonly used surveys for assessing well-being by the medical community. In addition, we present a case study that highlights how errors in assessment due to the subjective nature of the responses could potentially be avoided by continuous sensing and inference of social interactions and physical activities.
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“Furthermore, our application detects sleep [17] and speech/conversation data [54, 76] by incorporating pre-trained classifiers using accelerometer and audio data from a microphone, respectively.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [76]
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Cites
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454
Screening for Depression in Well Older Adults: Evaluation of a Short Form of the CES-D
Andresen, E. M., Malmgren, J. A., Carter, W. B., & Patrick, D. L. (1994) · American Journal of Preventive Medicine · doi:10.1016/s0749-3797(18)30622-6 · Cited by 5,403
Andresen, E. M., Malmgren, J. A., Carter, W. B., & Patrick, D. L. (1994) · PubMed · Cited by 4,426
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147
Using Pedometers to Increase Physical Activity and Improve Health: A Systematic Review
Bravata, D. M., Smith-Spangler, C., Sundaram, V., Gienger, A. L., Lin, N., Lewis, R., Stave, C. D., Olkin, I., & Sirard, J. R. (2007) · JAMA · doi:10.1001/jama.298.19.2296 · Cited by 2,064
International Journal of Geriatric Psychiatry
Robinson, L., Hutchings, D., Dickinson, H. O., Corner, L., Beyer, F., Finch, T. L., Hughes, J., Vanoli, A., Ballard, C. G., & Bond, J. (2006) · International Journal of Geriatric Psychiatry · doi:10.1002/(issn)1099-1166 · Cited by 1,301
Social ties and health: The benefits of social integration
Seeman, T. E. (1996) · Annals of Epidemiology · doi:10.1016/s1047-2797(96)00095-6 · Cited by 1,137
Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J. E., Harrison, B. L., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., et al. (2008) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357335 · Cited by 1,133
A Prospective Study of Physical Activity and Cognitive Decline in Elderly Women: Women Who Walk
Yaffe, K., Barnes, D., Nevitt, M., Lui, L.-Y., & Covinsky, K. (2001) · Archives of Internal Medicine · doi:10.1001/archinte.161.14.1703 · Cited by 921
Kirkman, B. L., Rosen, B., Tesluk, P. E., & Gibson, C. B. (2004) · Academy of Management Journal · doi:10.2307/20159571 · Cited by 889
A survey for assessing physical activity among older adults
DiPietro, L., Caspersen, C. J., Ostfeld, A. M., & NADEL, E. R. (1993) · Medicine & Science in Sports & Exercise · doi:10.1249/00005768-199305000-00016 · Cited by 708
Sharing Visual Features for Multiclass and Multiview Object Detection
Torralba, A. B., Murphy, K. P., & Freeman, W. T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1055 · Cited by 704
The 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 608
Self-citationSomatization and the recognition of depression and anxiety in primary care
Kirmayer, L. J., Robbins, J. M., Dworkind, M., & Yaffe⃰, M. J. (1993) · American Journal of Psychiatry · doi:10.1176/ajp.150.5.734 · Cited by 601
Dellaert, F., Polzin, T., & Waibel, A. (1996) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.608022 · Cited by 578
Health care costs associated with depressive and anxiety disorders in primary care
(1995) · American Journal of Psychiatry · doi:10.1176/ajp.152.3.352 · Cited by 555 · cited 1×
“Depression, a common psychiatric disorder, has a prevalence of 5-25% [23, 24] and contributes significantly to the cost of healthcare [25, 26].”
BackgroundAcoustical properties of speech as indicators of depression and suicidal risk
France, D. J., Shiavi, R. G., Silverman, S., Silverman, M., & Wilkes, M. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.846676 · Cited by 504
Sheridan, P. L., Solomont, J., Kowall, N. W., & Hausdorff, J. M. (2003) · Journal of the American Geriatrics Society · doi:10.1046/j.1532-5415.2003.51516.x · Cited by 493
A Practical Approach to Recognizing Physical Activities
Lester, J., Choudhury, T., & Borriello, G. (2006) · Lecture notes in computer science · doi:10.1007/11748625_1 · Cited by 472
Self-citationCommunicating emotion: The role of prosodic features.
Frick, R. W. (1985) · Psychological Bulletin · doi:10.1037/0033-2909.97.3.412 · Cited by 467
Hawthorne, G. (2006) · Social Indicators Research · doi:10.1007/s11205-005-7746-y · Cited by 450
A comparison of coping assessed by ecological momentary assessment and retrospective recall.
Stone, A. A., Schwartz, J. E., Neale, J. M., Shiffman, S. M., Marco, C. A., Hickcox, M., Paty, J. A., Porter, L. S., & Cruise, L. J. (1998) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.74.6.1670 · Cited by 446
Olguin, D. O., Waber, B. N., Kim, T. J., Mohan, A., Ara, K., & Pentland, A. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2006638 · Cited by 417
Mental Disorders and Disability Among Patients in a Primary Care Group Practice
Olfson, M., FIREMAN, B. H., Weissman, M. M., Leon, A. C., Sheehan, D. V., Kathol, R. G., Hoven, C. W., & Farber, L. H. (1997) · American Journal of Psychiatry · doi:10.1176/ajp.154.12.1734 · Cited by 404
A Hybrid Discriminative/Generative Approach for Modeling Human Activities
Lester, J., Choudhury, T., Kern, N., Borriello, G., & Hannaford, B. (2005) · TUbilio (Technical University of Darmstadt) · Cited by 402
Self-citationProceedings of Interspeech 2008
Moore, J. D., Tietze, M., & Demberg, V. (2008) · Cited by 353
A comparison of coping assessed by ecological momentary assessment and retrospective recall.
Stone, A. A., Schwartz, J. E., Neale, J. M., et al. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.74.6.1670 · Cited by 346
Tudor-Locke, C. E., Sisson, S. B., Collova, T., Lee, S. M., & Swan, P. D. (2005) · Canadian Journal of Applied Physiology · doi:10.1139/h05-147 · Cited by 287
Personality Moderators of Reactions to Interpersonal Rejection: Depression and Trait Self-Esteem
Nezlek, J. B., Kowalski, R. M., Leary, M. R., Blevins, T. L., & Holgate, S. (1997) · Personality and Social Psychology Bulletin · doi:10.1177/01461672972312001 · Cited by 286
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
Moore, E., Clements, M. A., Peifer, J. W., & Weisser, L. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.900562 · Cited by 218
Use of ecological momentary assessment in mood disorders research
Wenze, S. J., & Miller, I. W. (2010) · Clinical Psychology Review · doi:10.1016/j.cpr.2010.06.007 · Cited by 205
Validity of Physical Activity Intensity Predictions by ActiGraph, Actical, and RT3 Accelerometers
Rothney, M. P., Schaefer, E. V., Neumann, M. M., Choi, L., & Chen, K. Y. (2008) · Obesity · doi:10.1038/oby.2008.279 · Cited by 175
Does trait coping exist? A momentary assessment approach to the evaluation of traits.
Schwartz, J. E., Neale, J. M., Marco, C. A., Shiffman, S. S., & Stone, A. A. (1999) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.77.2.360 · Cited by 160
Detection of synthetic speech for the problem of imposture
Leon, P. L. D., Hernáez, I., Saratxaga, I., Pucher, M., & Yamagishi, J. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5947440 · Cited by 159
Henk, H. J. (1996) · Archives of General Psychiatry · doi:10.1001/archpsyc.1996.01830100045006 · Cited by 130
Executive Dysfunction and Disability in Elderly Patients With Major Depression
Kiosses, D. N., Klimstra, S., Murphy, C., & Alexopoulos, G. S. (2001) · American Journal of Geriatric Psychiatry · doi:10.1097/00019442-200108000-00011 · Cited by 129
Automatic statistical analysis of the signal and prosodic signs of emotion in speech
Cowie, R., & Douglas‐Cowie, E. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.608027 · Cited by 126
Detecting Group Interest-Level in Meetings
Gática-Pérez, D., McCowan, I. A., Zhang, D., & Samy Bengio, A. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415157 · Cited by 125
Expressed emotion and interactional patterns in the families of bipolar patients.
Simoneau, T. L., Miklowitz, D. J., & Saleem, R. (1998) · Journal of Abnormal Psychology · doi:10.1037/0021-843x.107.3.497 · Cited by 118
Psychosocial and clinical predictors of unipolar depression outcome in older adults
Bosworth, H. B., Hays, J. C., George, L. K., & Steffens, D. C. (2002) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.590 · Cited by 110
Age‐related Brain Changes Associated with Motor Function in Healthy Older People
Camicioli, R. M., Moore, M. M., Sexton, G. J., Howieson, D. B., & Kaye, J. A. (1999) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1999.tb02997.x · Cited by 108
Isaac, V., Stewart, R. J., Artéro, S., Ancelin, M., & Ritchie, K. A. (2009) · American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e3181a88441 · Cited by 107
Daily Routine Recognition through Activity Spotting
Blanke, U., & Schiele, B. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01721-6_12 · Cited by 79
Wyatt, D., Choudhury, T., Bilmes, J., & Kitts, J. A. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1889681.1889688 · Cited by 69
Self-citationValidity of 2 Devices for Measuring Steps Taken by Older Adults in Assisted-Living Facilities
Bergman, R. J., Bassett, D. R., & Klein, D. A. (2008) · Journal of Physical Activity and Health · doi:10.1123/jpah.5.s1.s166 · Cited by 62
A linked-HMM model for robust voicing and speech detection
Basu, S. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2003.1198906 · Cited by 56
Conversation detection and speaker segmentation in privacy-sensitive situated speech data
Wyatt, D., Choudhury, T., & Bilmes, J. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-256 · Cited by 54
Self-citationBang, S.-L., Kim, M., Song, S.-K., & Park, S.-J. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4650386 · Cited by 43
Conrad, A., Wilhelm, F. H., Roth, W. T., Spiegel, D., & Taylor, C. B. (2008) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2007.08.003 · Cited by 41
Hurlburt, R. T., Koch, M. G., & Heavey, C. L. (2002) · Cognitive Therapy and Research · doi:10.1023/a:1013849922756 · Cited by 41
Towards the automated social analysis of situated speech data
Wyatt, D., Bilmes, J., Choudhury, T., & Kitts, J. A. (2008) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/1409635.1409658 · Cited by 35
Self-citationTechnology and Long-term Care (TLC): A pilot evaluation of remote monitoring of elders
Reder, S., Ambler, G., Philipose, M., & Hedrick, S. C. (2010) · Gerontechnology · doi:10.4017/gt.2010.09.01.002.00 · Cited by 21
Flagged first, then by citations
Research works printed but not linked: 4
- Sf-36 , http://www.sf-36.org , 2008 . Sf-36, http://www.sf-36.org, 2008.
- 10.1145/1889681.1889688 · DOI
- Tanzeem Choudhury and Sumit Basu . Modeling conversational dynamics as a mixed-memory markov process . In Proc. of NIPS , 2004 . Tanzeem Choudhury and Sumit Basu. Modeling conversational dynamics as a mixed-memory markov process. In Proc. of NIPS, 2004.
- Jerome Friedman , Trevor Hastie , and Robert Tibshirani . Additive logistic regression: a statistical view of boosting . Annals of Statistics , 28 : 2000 , 1998 . Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28:2000, 1998.
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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
Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047
Torous, J., Kiang, M. V., Lorme, J., & Onnela, J.-P. (2016) · JMIR Mental Health · doi:10.2196/mental.5165 · Cited by 758
“Accelerometer data could be useful in helping monitor for neurological side effects of medications, such as tremors present when the subject is holding the phone to make a call and also in understanding activity states (eg, running vs walking) [22].”
BackgroundSaeb, 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
Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges
Majumder, S., Aghayi, E., Noferesti, M., Memarzadeh‐Tehran, H., Mondal, T. K., Pang, Z., & Deen, M. J. (2017) · Sensors · doi:10.3390/s17112496 · Cited by 636
Metadata
- Authors
- Rabbi, M., Ali, S., Choudhury, T., & Berke, E.
- Affiliations
- Cornell University (US)
- Dartmouth Medical School, Lebanon, NH, USA
- Venue
- Proceedings of the 13th international conference on Ubiquitous computing
- Publisher
- ACM
- Published
- 2011-09-17
- Volume
- 2011
- Pages
- 385-394
- PMC
- PMC4180507
- OpenAlex
- W2161897659
- Cited by
- 243
- References
- 55
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 15 · 2023: 25 · 2022: 18 · 2021: 22 · 2020: 27 · 2019: 15 · 2018: 16 · 2017: 23 · 2016: 17 · 2015: 13 · 2014: 17 · 2013: 14 · 2012: 9
- FWCI
- 9.52
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institute on Aging (K23AG036934)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- well-being assessment, mental health, physical health, mobile sensing, in situ measurement, continuous monitoring, multimodal sensing, motion sensing, audio sensing, physical activity
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