The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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.
Cited by
Sorted by citations
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
Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616650285 · Cited by 515
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
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406
Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing
Wang, R., Wang, W., daSilva, A., Huckins, J. F., Kelley, W. M., Heatherton, T. F., & Campbell, A. T. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3191775 · Cited by 340
Lane, N. D., Georgiev, P., & Qendro, L. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2804262 · Cited by 310
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
Self-citationEdge Intelligence: Empowering Intelligence to the Edge of Network
Xu, D., Li, T., Li, Y., Su, X. J., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3119950 · Cited by 296
Smartphone sensing methods for studying behavior in everyday life
Harari, G. M., Müller, S. R., Aung, M. H., & Rentfrow, P. J. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.018 · Cited by 263
Mobile Health: Revolutionizing Healthcare Through Transdisciplinary Research
Kumar, S., Nilsen, W., Misha Pavel, C., & Srivastava, M. B. (2013) · Computer · doi:10.1109/mc.2012.392 · Cited by 243
Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., & Kawsar, F. (2015) · doi:10.1145/2820975.2820980 · Cited by 236
A Survey of Mobile Crowdsensing Techniques
Liu, J., Shen, H., Narman, H. S., Chung, W., & Lin, Z. (2018) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3185504 · Cited by 209
A Revised Sociogenomic Model of Personality Traits
Roberts, B. W. (2017) · Journal of Personality · doi:10.1111/jopy.12323 · Cited by 216
Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks
Dawadi, P., Cook, D. J., & Schmitter‐Edgecombe, M. (2013) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2013.2252338 · Cited by 186
Thieme, A., Hanratty, M., Lyons, M., Palacios, J. E., Marques, R. F., Morrison, C. P., & Doherty, G. J. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3564752 · Cited by 197
Harari, G. M., Müller, S. R., Stachl, C., Wang, R., Wang, W., Bühner, M., Rentfrow, P. J., Campbell, A. T., & Gosling, S. D. (2019) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000245 · Cited by 168
Seppälä, J., De Vita, I., Jämsä, T., Miettunen, J., Isohanni, M., Rubinstein, K., Feldman, Y., Grasa, E., Corripio, I., Berdun, J., et al. (2019) · JMIR Mental Health · doi:10.2196/mental.9819 · Cited by 148
DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks
Suhara, Y., Xu, Y., & Pentland, A. '. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052676 · Cited by 163
Pejović, V., & Musolesi, M. (2015) · ACM Computing Surveys · doi:10.1145/2693843 · Cited by 163
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Pejović, V., & Musolesi, M. (2013) · Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · doi:10.48550/arxiv.1306.2356 · Cited by 151
A Survey of Human Gait-Based Artificial Intelligence Applications
Harris, E., Khoo, I., & Demircan, E. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.749274 · Cited by 136
Edge Intelligence: Architectures, Challenges, and ApplicationsPreprint
Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12172 · Cited by 204
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
Self-citationAbdullah, 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 127
Self-citationUser Verification Leveraging Gait Recognition for Smartphone Enabled Mobile Healthcare Systems
Ren, Y., Chen, Y., Chuah, M. C., & Yang, J. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2014.2365185 · Cited by 123
Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques
Cook, D. J., Schmitter-Edgecombe, M., & Dawadi, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2461659 · Cited by 123
Xu, C., Li, S., Liu, G., Zhang, Y., Miluzzo, E., Chen, Y.-F. R., Li, J., & Firner, B. (2013) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2493432.2493435 · Cited by 122
Xiong, H., Huang, Y., Barnes, L. E., & Gerber, M. S. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971711 · Cited by 116
Sensing Technologies for Monitoring Serious Mental Illnesses
Abdullah, S., & Choudhury, T. (2018) · IEEE Multimedia · doi:10.1109/mmul.2018.011921236 · Cited by 116
Self-citationObjective assessment of depressive symptoms with machine learning and wearable sensors data
Ghandeharioun, A., Fedor, S., Sangermano, L., Ionescu, D., Alpert, J., Dale, C., Sontag, D., & Picard, R. (2017) · 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2017.8273620 · Cited by 116
Using unlabeled data in a sparse-coding framework for human activity recognition
Bhattacharya, S., Nurmi, P., Hammerla, N., & Plötz, T. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2014.05.006 · Cited by 116
Lowering the barriers to large-scale mobile crowdsensing
Xiao, Y., Simoens, P., Pillai, P. S., Ha, K., & Satyanarayanan, M. (2013) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/2444776.2444789 · Cited by 108
Vilardaga, R., Rizo, J., Zeng, E., Kientz, J. A., Ries, R., Otis, C., & Hernandez, K. (2018) · JMIR Serious Games · doi:10.2196/games.8881 · Cited by 90
Automated assessment of cognitive health using smart home technologies
Dawadi, P., Cook, D. J., Schmitter‐Edgecombe, M., & Parsey, C. M. (2013) · Technology and Health Care · doi:10.3233/thc-130734 · Cited by 114
Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing
Wang, R., Wang, W., Aung, M. H., Ben‐Zeev, D., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J. M., Scherer, E. A., et al. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130976 · Cited by 105
Self-citationAdler, D. A., Ben‐Zeev, D., Tseng, V. W.-S., Kane, J. M., Brian, R., Campbell, A. T., Hauser, M., Scherer, E. A., & Choudhury, T. (2020) · JMIR mhealth and uhealth · doi:10.2196/19962 · Cited by 107
Self-citationWang, W., Harari, G. M., Wang, R., Müller, S. R., Mirjafari, S., Masaba, K., & Campbell, A. T. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3264951 · Cited by 100
From Smart to Cognitive Phones
Campbell, A. T., & Choudhury, T. (2012) · IEEE Pervasive Computing · doi:10.1109/mprv.2012.41 · Cited by 97
Self-citationHarari, G. M., Gosling, S. D., Wang, R., Chen, F., Chen, Z., & Campbell, A. T. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.10.027 · Cited by 104
Sultana, S., & Ahmed, S. I. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300586 · Cited by 92
Privacy-Preserving Data Aggregation in Mobile Phone Sensing
Zhang, Y., Chen, Q., & Zhong, S. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2515513 · Cited by 89
Mobile crowd sensing – Taxonomy, applications, challenges, and solutions
Boubiche, D. E., Imran, M. A., Maqsood, A., & Shoaib, M. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.10.028 · Cited by 85
Aung, M. H., Matthews, M. L., & Choudhury, T. (2017) · Depression and Anxiety · doi:10.1002/da.22646 · Cited by 84
Self-citationTracking human queues using single-point signal monitoring
Wang, Y., Yang, J., Chen, Y. J., Liu, H., Gruteser, M., & Martin, R. P. (2014) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2594368.2594382 · Cited by 82
Smartphone based user verification leveraging gait recognition for mobile healthcare systems
Ren, Y., Chen, Y. J., Chuah, M. C., & Yang, J. (2013) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2013.6644973 · Cited by 81
Rawtaer, I., Mahendran, R., Kua, E. H., Tan, H.-P., Tan, H., Lee, T.-S. W., & Ng, T. P. (2020) · Journal of Medical Internet Research · doi:10.2196/16854 · Cited by 80
In Situ Cues for ADHD Parenting Strategies Using Mobile Technology
Pina, L. R., Rowan, K., Johns, P., Roseway, A., Hayes, G. R., & Czerwinski, M. P. (2014) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2014.254958 · Cited by 83
Nicholas, J., Shilton, K., Schueller, S. M., Gray, E. L., Kwasny, M. J., & Mohr, D. C. (2019) · JMIR mHealth and uHealth · doi:10.2196/12578 · Cited by 77
Bai, R., Xiao, L., Guo, Y., Zhu, X., Li, N., Wang, Y., Chen, Q., Feng, L., Wang, Y., Yu, X., et al. (2021) · JMIR mhealth and uhealth · doi:10.2196/24365 · Cited by 77
Zhang, R., Ringland, K. E., Paan, M., Mohr, D. C., & Reddy, M. C. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445771 · Cited by 81
Adler, D. A., Wang, F., Mohr, D. C., & Choudhury, T. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266516 · Cited by 90
Self-citationKEH-Gait: Using Kinetic Energy Harvesting for Gait-based User Authentication Systems
Xu, W., Lan, G., Lin, Q., Khalifa, S., Hassan, M., Bergmann, N. W., & Hu, W. S. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2828816 · Cited by 74
Mehrotra, A., Tsapeli, F., Hendley, R., & Musolesi, M. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130948 · Cited by 73
Does Smartphone Use Drive our Emotions or vice versa? A Causal Analysis
Sarsenbayeva, Z., Marini, G., van Berkel, N., Luo, C., Jiang, W., Yang, K., Wadley, G., Dingler, T., Kostakos, V., & Gonçalves, J. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376163 · Cited by 72
A Scoping Review of Technological Approaches to Environmental Monitoring
Coulby, G., Clear, A. K., Jones, O., & Godfrey, A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17113995 · Cited by 69
Chikersal, P., Belgrave, D. C. M., Doherty, G. J., Enrique, Á., Palacios, J. E., Richards, D., & Thieme, A. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376341 · Cited by 68
Tracking Mental Well-Being: Balancing Rich Sensing and Patient Needs
Matthews, M., Abdullah, S., Gay, G., & Choudhury, T. (2014) · Computer · doi:10.1109/mc.2014.107 · Cited by 64
Self-citationKornfield, R., Zhang, R., Nicholas, J., Schueller, S. M., Cambo, S. A., Mohr, D. C., & Reddy, M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376309 · Cited by 64
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Wang, Z., Xiong, H., Zhang, J., Yang, S., Boukhechba, M., Zhang, D., Barnes, L. E., & Dou, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3161046 · Cited by 63
Kawakami, A., Chowdhary, S., Iqbal, S. T., Liao, Q. V., Olteanu, A., Suh, J., & Saha, K. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610207 · Cited by 79
Gyrard, A., & Sheth, A. (2020) · Smart Health · doi:10.1016/j.smhl.2019.100083 · Cited by 48
Recent Advances in Contactless Sensing Technologies for Mental Health Monitoring
Nouman, M., Khoo, S., Mahmud, M. A. P., & Kouzani, A. Z. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3097801 · Cited by 60
Natural Language Processing as an Emerging Tool to Detect Late-Life Depression
DeSouza, D. D., Robin, J., Gumus, M., & Yeung, A. T. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.719125 · Cited by 59
Sarda, A., Munuswamy, S., Sarda, S., & Subramanian, V. (2019) · JMIR mHealth and uHealth · doi:10.2196/11041 · Cited by 56
Multi-Modal Mobile Sensing of Social Interactions
Matic, A., Osmani, V., Maxhuni, A., & Mayora, O. (2012) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2012.248689 · Cited by 55
Xu, W., Shen, Y., Zhang, Y., Bergmann, N. W., & Hu, W. S. (2017) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3054977.3054991 · Cited by 55
Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression
Farhan, A. A., Lu, J., Bi, J., Russell, A., Wang, B., & Bamis, A. (2016) · 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2016.27 · Cited by 61
Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection
Yang, M., Ngai, E. C. ‐., Hu, X., Hu, B., Liu, J., Erol Gelenbe, A., & Leung, V. C. M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3542324 · Cited by 56
Birbeck, N., Lawson, S. W., Morrissey, K., Rapley, T., & Olivier, P. L. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025931 · Cited by 53
KEH-Gait: Towards a Mobile Healthcare User Authentication System by Kinetic Energy Harvesting
Xu, W., Lan, G., Lin, Q., Khalifa, S., Bergmann, N. W., Hassan, M., & Hu, W. S. (2017) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2017.23023 · Cited by 53
StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G. M., Tignor, S. M., Zhou, X., Ben‐Zeev, D., & Campbell, A. T. (2017) · Springer eBooks · doi:10.1007/978-3-319-51394-2_2 · Cited by 53
Personality Sensing for Theory Development and Assessment in the Digital Age
Harari, G. M., Vaid, S. S., Müller, S. R., Stachl, C., Marrero, Z., Schoedel, R., Bühner, M., & Gosling, S. D. (2020) · European Journal of Personality · doi:10.1002/per.2273 · Cited by 61
Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study
DeMasi, O., Feygin, S., Dembo, A., Aguilera, A., & Recht, B. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7820 · Cited by 56
The latency, accuracy, and battery (LAB) abstraction
Kansal, A., Saponas, S., Brush, A. J. B., McKinley, K. S., Mytkowicz, T., & Ziola, R. (2013) · Conference on Object-Oriented Programming Systems, Languages, and Applications · doi:10.1145/2509136.2509541 · Cited by 48
Zhao, G., Liu, Y., & Shi, Y. (2018) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2018.2803025 · Cited by 47
Rabbi, M., Philyaw-Kotov, M. L., Cunningham, R. M., Bonar, E. E., Nahum-Shani, I. B., Klasnja, P., Walton, M. A., & Murphy, S. A. (2018) · JMIR Research Protocols · doi:10.2196/resprot.9850 · Cited by 48
Self-citationWang, W., Mirjafari, S., Harari, G. M., Ben‐Zeev, D., Brian, R., Choudhury, T., Hauser, M., Kane, J. M., Masaba, K., Nepal, S. K., et al. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376855 · Cited by 47
Self-citationLightweight Privacy-Preserving Raw Data Publishing Scheme
Chen, J., Liu, G., & Liu, Y. (2020) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2020.2974183 · Cited by 46
Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia
Tseng, V. W.-S., Sano, A., Ben-Zeev, D., Brian, R., Campbell, A. T., Hauser, M., Kane, J. M., Scherer, E. A., Wang, R., Wang, W., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-71689-1 · Cited by 44
Self-citationPredicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data
Rashid, H., Mendu, S., Daniel, K. E., Beltzer, M., Teachman, B. A., Boukhechba, M., & Barnes, L. E. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3411823 · Cited by 49
Using ML and Data-Mining Techniques in Automatic Vulnerability Software Discovery
(2021) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2021/871032021 · Cited by 40
Crowdsensing the speaker count in the wild: implications and applications
Xu, C., Li, S., Zhang, Y., Miluzzo, E., & Chen, Y.-F. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6917408 · Cited by 39
Gait-Watch: A Gait-based context-aware authentication system for smart watch via sparse coding
Xu, W., Shen, Y., Luo, C., Li, J., Li, W., & Zomaya, A. Y. (2020) · Ad Hoc Networks · doi:10.1016/j.adhoc.2020.102218 · Cited by 38
Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain
Aung, M. H., Alquaddoomi, F., Hsieh, C.-K., Rabbi, M., Yang, L., Pollak, J., Estrin, D. L., & Choudhury, T. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2565381 · Cited by 38
Self-citationUse of Computer and Mobile Technologies in the Treatment of Depression
Callan, J. A., Wright, J. H., Siegle, G. J., Howland, R. H., & Kepler, B. B. (2016) · Archives of Psychiatric Nursing · doi:10.1016/j.apnu.2016.10.002 · Cited by 41
TigerAware: An Innovative Mobile Survey and Sensor Data Collection and Analytics System
Morrison, W., Guerdan, L., Kanugo, J., Trull, T. J., & Shang, Y. (2018) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2018.00025 · Cited by 37
Petsolari, M., Ibrahim, S. B., & Slovak, P. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642619 · Cited by 35
Semantic Gap in Predicting Mental Wellbeing through Passive Sensing
Swain, V. D., Chen, V., Mishra, S., Mattingly, S. M., Abowd, G. D., & Choudhury, M. D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502037 · Cited by 35
Lazarou, I., Stavropoulos, T. G., Μεδίτσκος, Γ., Andreadis, S. T., Kompatsiaris, I. Y., & Tsolaki, M. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190423 · Cited by 38
Gyrard, A., Tabeau, K., Fiorini, L., Kung, A., Sengès, É., de Mul, M., Giuliani, F., Lefebvre, D., Hoshino, H., Fabbricotti, I. N., et al. (2021) · International Journal of Social Robotics · doi:10.1007/s12369-021-00821-6 · Cited by 32
Food, Mood, Context: Examining College Students’ Eating Context and Mental Well-being
Morshed, M. B., Kulkarni, S. S., Saha, K., Li, R. C., Roper, L. G., Nachman, L., Lu, H., Mirabella, L., Srivastava, S., de Barbaro, K., et al. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3533390 · Cited by 34
Pearl: A Technology Probe for Machine-Assisted Reflection on Personal Data
Jörke, M., Sefidgar, Y. S., Massachi, T., Suh, J., & Ramos, G. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584054 · Cited by 34
Digital phenotyping for mental health based on data analytics: A systematic literature review
Heckler, W. F., Feijó, L. P., de Carvalho, J. V., & Barbosa, J. L. V. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103094 · Cited by 32
Arora, A., Chakraborty, P., & Bhatia, M. P. S. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06078-5 · Cited by 32
Toward explainable AI-empowered cognitive health assessment
Javed, A. R., Khan, H. U., Alomari, M. K., Sarwar, M. U., Asim, M., Almadhor, A. S., & Khan, M. Z. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1024195 · Cited by 36
Predicting Brain Functional Connectivity Using Mobile Sensing
Obuchi, M., Huckins, J. F., Wang, W., daSilva, A., Rogers, C., Murphy, E., Hedlund, E., Holtzheimer, P., Mirjafari, S., & Campbell, A. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3381001 · Cited by 29
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
