Passive and In-Situ assessment of mental and physical well-being using mobile sensors

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

Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)

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

Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale).

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

Activity sensing in the wild

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

The impact and measurement of social dysfunction in late‐life depression: an evaluation of current methods with a focus on wearable technology

Hodgetts, S., Gallagher, P., Stow, D., Ferrier, I. N., & O'Brien, J. T. (2016) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.4632 · Cited by 31

Enhancement of Neurocognitive Assessments Using Smartphone Capabilities: Systematic Review

Templeton, J. M., Poellabauer, C., & Schneider, S. L. (2020) · JMIR mhealth and uhealth · doi:10.2196/15517 · Cited by 29

Speech activity detection using accelerometer

Matic, A., Osmani, V., & Mayora, O. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346377 · Cited by 28

CoenoFire

Feese, S., Bert Arnrich, A., Tröster, G., Burtscher, M. J., Meyer, B. M., & Jonas, K. W. (2013) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2493432.2493450 · Cited by 28

Approaches for tailoring between-session mental health therapy activities

Oewel, B., Areán, P. A., & Agapie, E. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642856 · Cited by 33

Direct Exposure to Green and Blue Spaces is Associated with Greater Mental Wellbeing in Older Adults

Jarosz, E. (2022) · Journal of Aging and Environment · doi:10.1080/26892618.2022.2109792 · Cited by 31

FamilyLog: A mobile system for monitoring family mealtime activities

Bi, C., Xing, G., Tian, H., Huh, J., Peng, W., & Ma, M. (2017) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2017.7917847 · Cited by 25

Novel technology as platform for interventions for caregivers and individuals with severe mental health illnesses: A systematic review

Meyer, T. D., Casarez, R. L., Mohite, S., Rosa, N. L. L., & Iyengar, M. S. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.09.012 · Cited by 30

DepreST-CAT

Tlachac, M., Flores, R., Reisch, M., Houskeeper, K., & Rundensteiner, E. A. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534596 · Cited by 24

The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health

Kruzan, K. P., Ng, A., Stiles‐Shields, C., Lattie, E. G., Mohr, D. C., & Reddy, M. C. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581209 · Cited by 24

Energizing organizational research: Advancing the energy field with group concepts and theories

Deline, M. B. (2015) · Energy Research & Social Science · doi:10.1016/j.erss.2015.06.003 · Cited by 27

Federated Meta-Learning with Attention for Diversity-Aware Human Activity Recognition

Shen, Q., Feng, H., Song, R., Song, D., & Xu, H. (2023) · Sensors · doi:10.3390/s23031083 · Cited by 24

Dictionary and deep learning algorithms with applications to remote health monitoring systems

Mathews, S. M. (2024) · Library, Museums and Press - UDSpace (University of Delaware) · doi:10.58088/m1tr-1918 · Cited by 23

Sensing spatial and temporal coordination in teams using the smartphone

Feese, S., Burtscher, M. J., Jonas, K. W., & Tröster, G. (2014) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-014-0015-9 · Cited by 22

ReVibe

Rabbi, M., Li, K. T., Yan, H. Y., Dahl, K. L., Klasnja, P., & Murphy, S. A. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369806 · Cited by 22

Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research

Yoo, D. W., Woo, H., Pendse, S. R., Lu, N. Y., Birnbaum, M. L., Abowd, G. D., & Choudhury, M. D. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637372 · Cited by 23

Using opportunistic face logging from smartphone to infer mental health

Wang, R., Campbell, A. T., & Zhou, X. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2804391 · Cited by 22

Tracking Fatigue and Health State in Multiple Sclerosis Patients Using Connnected Wellness Devices

Tong, C., Craner, M. J., Vegreville, M., & Lane, N. D. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351264 · Cited by 20

My smartphone knows i am hungry

Chen, F., Wang, R., Zhou, X., & Campbell, A. T. (2014) · doi:10.1145/2611264.2611270 · Cited by 20

Shaping the Design of Smartphone-Based Interventions for Self-Harm

Honary, M., Bell, B. T., Clinch, S., Vega, J., Kroll, L., Sefi, A., & McNaney, R. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376370 · Cited by 21

Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity

Demasi, Aguilera, A., & Recht (2016) · Wireless Health · doi:10.1109/wh.2016.7764552 · Cited by 21

Situating smartphones in daily life: Big Five traits and contexts associated with young adults’ smartphone use.

Roehrick, K., Vaid, S. S., & Harari, G. M. (2023) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000478 · Cited by 22

Automation of feature engineering for IoT analytics

Banerjee, S., Chattopadhyay, T., Pal, A., & Garain, U. (2018) · ACM SIGBED Review · doi:10.1145/3231535.3231538 · Cited by 18

A Study on Mobile Crowd Sensing Systems for Healthcare Scenarios

Zhang, E., Trujillo, R., Templeton, J. M., & Poellabauer, C. (2023) · IEEE Access · doi:10.1109/access.2023.3342158 · Cited by 20

Survey of Sensor-Based Personal Wellness Management Systems

Yumak, Z., & Pu, P. (2013) · BioNanoScience · doi:10.1007/s12668-013-0099-0 · Cited by 18

FamilyLog: Monitoring Family Mealtime Activities by Mobile Devices

Bi, C., Xing, G., Tian, H., Huh, J., Peng, W., Ma, M., & Chang, X. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2916357 · Cited by 17

Mobile RDoC: Using Smartphones to Understand the Relationship Between Auditory Verbal Hallucinations and Need for Care

Ben‐Zeev, D., Buck, B., Chander, A., Brian, R., Wang, W., Atkins, D. C., Brenner, C. J., Cohen, T., Campbell, A. T., & MUNSON, J. A. (2020) · Schizophrenia Bulletin Open · doi:10.1093/schizbullopen/sgaa060 · Cited by 16

Does Your Smartphone “Know” Your Social Life? A Methodological Comparison of Day Reconstruction, Experience Sampling, and Mobile Sensing

Roos, Y., Krämer, M. D., Richter, D., Schoedel, R., & Wrzus, C. (2023) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459231178738 · Cited by 15

Internet of Things for Emotion Care: Advances, Applications, and Challenges

Xu, X., Fu, C., Camacho, D., Park, J. H., & Chen, J. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10327-8 · Cited by 16

A System to Analyze Group Socializing Behaviors in Social Parties

Jang, H., Choe, S. P., Gunkel, S. N. B., Kang, S., & Song, J. (2016) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2016.2634918 · Cited by 15

Using Mobile Sensors to Study Personality Dynamics

Wiernik, B. M., Öneş, D. S., Marlin, B. M., Giordano, C., Dilchert, S., Mercado, B. K., Stanek, K. C., Birkland, A. S., Wang, Y., Ellis, B. D., et al. (2020) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000576 · Cited by 18

Sensus: A Cross-Platform, General-Purpose System for Mobile Crowdsensing in Human-Subject StudiesPreprint

Xiong, H., Huang, Y., Barnes, L. E., & Gerber, M. S. (2021) · doi:10.31234/osf.io/uste8 · Cited by 15

Towards Health Recommendation Systems: An Approach for Providing Automated Personalized Health Feedback from Mobile Data

Rabbi, M., Aung, M. H., & Choudhury, T. (2017) · Springer eBooks · doi:10.1007/978-3-319-51394-2_26 · Cited by 15

Ethical Considerations for Augmenting Surveys with Auxiliary Data Sources

Struminskaya, B., & Sakshaug, J. W. (2023) · Public Opinion Quarterly · doi:10.1093/poq/nfad030 · Cited by 14

Impact of mood changes on application selection

Visuri, A., Sarsenbayeva, Z., Gonçalves, J., Karapanos, E., & Jones, S. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968317 · Cited by 13

Social dynamics and affect: Investigating within-person associations in daily life using experience sampling and mobile sensing.

Krämer, M. D., Roos, Y., Schoedel, R., Wrzus, C., & Richter, D. (2023) · Emotion · doi:10.1037/emo0001309 · Cited by 16

Examining and Promoting Explainable Recommendations for Personal Sensing Technology Acceptance

Newn, J., Kelly, R., D’Alfonso, S., & Lederman, R. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3550297 · Cited by 15

A context-aware do-not-disturb service for mobile devices

Qin, Y., Bhattacharya, T., Kulik, L., & Bailey, J. (2014) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/2677972.2678003 · Cited by 12

WatchOver

Kunchay, S., & Abdullah, S. (2020) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3410530.3414373 · Cited by 12

Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness

Elmer, T., & Lodder, G. M. A. (2022) · Journal of Social and Personal Relationships · doi:10.1177/02654075221122069 · Cited by 15

Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study

Kadirvelu, B., Bel, T. B., Wu, X., Burmester, V., Ananth, S., Branco, B. C. C. C., Serrano, B. G., Gledhill, J. A., Simplicio, M. C. D., Nicholls, D., et al. (2023) · JMIR Formative Research · doi:10.2196/44877 · Cited by 12

Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone Sensors

Champa, A. I., Rabbi, M. F., Eishita, F. Z., & Zibran, M. F. (2023) · IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA) · doi:10.1109/sera57763.2023.10197713 · Cited by 11

Automated Face-To-Face Conversation Detection on a Commodity Smartwatch with Acoustic Sensing

Liang, D., Zhang, A., & Thomaz, E. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3610882 · Cited by 11

Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective

Fukazawa, Y., Yamamoto, N., Hamatani, T., Ochiai, K., Uchiyama, A., & Ohta, K. (2020) · Journal of Information Processing · doi:10.2197/ipsjjip.28.16 · Cited by 13

ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms

Mansoor, H., Gerych, W., Alajaji, A. S., Buquicchio, L., Chandrasekaran, K., Agu, E., & Rundensteiner, E. A. (2021) · Visual Informatics · doi:10.1016/j.visinf.2021.07.001 · Cited by 11

Behavior Modeling Approach for Forecasting Physical Functioning of People with Multiple Sclerosis

Antar, A. D., Kratz, A. L., & Banović, N. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580887 · Cited by 12

Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study

Sun, Y., Kargarandehkordi, A., Slade, C. R., Jaiswal, A., Busch, G., Guerrero, A. P. S., Phillips, K. T., & Washington, P. Y. (2023) · JMIR Research Protocols · doi:10.2196/46493 · Cited by 9

The latency, accuracy, and battery (LAB) abstraction

Kansal, A., Saponas, S., Brush, A. J. B., McKinley, K. S., Mytkowicz, T., & Ziola, R. (2013) · ACM SIGPLAN Notices · doi:10.1145/2544173.2509541 · Cited by 10

Privacy Preserving Market Schemes for Mobile Sensing

Zhang, Y., Mao, Y., Zhang, H., & Zhong, S. (2015) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2015.100 · Cited by 9

A Classification Framework of Mobile Health CrowdSensing Research

Tokosi, T. O., & Scholtz, B. M. (2019) · South African Institute of Computer Scientists and Information Technologists · doi:10.1145/3351108.3351113 · Cited by 9

A Tool for Capturing Smartphone Screen Text

Teng, S., D’Alfonso, S., & Kostakos, V. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642347 · Cited by 13

VADLite

Boateng, G., Santhanam, P., Lüscher, J., Scholz, U., & Kowatsch, T. (2019) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3341162.3346274 · Cited by 9

What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment

Poudyal, A., van Heerden, A. C., Hagaman, A. K., Islam, C., Thapa, A., Maharjan, S. M., Byanjankar, P., & Kohrt, B. A. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.633606 · Cited by 10

Automatic Sensing of Speech Activity and Correlation with Mood Changes

Matic, A., Osmani, V., & Mayora, O. (2012) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-642-32538-0_9 · Cited by 9

Emotion Recognition in the Real World: Passively Collecting and Estimating Emotions From Natural Speech Data of Individuals With Bipolar Disorder

Provost, E. M., Sperry, S. H., Tavernor, J., Anderau, S., Yocum, A. K., & McInnis, M. G. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3407683 · Cited by 10

Skin Sensitivity Assessment Using Smartphone Haptic Feedback

Torres, W. O., Abbott, M. E., Wang, Y.-Q., & Stuart, H. S. (2023) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2023.3328502 · Cited by 6

A New Method for Discovering Daily Depression from Tweets to Monitor Peoples Depression Status

Sadasivuni, S. T., & Zhang, Y.-Q. (2020) · 2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) · doi:10.1109/hccai49649.2020.00013 · Cited by 7

Mobile sensing for studying personality dynamics in daily life

Harari, G. M., Stachl, C., Müller, S. R., & Gosling, S. D. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-813995-0.00029-7 · Cited by 9

Continuous Context Inference on Mobile Platforms

Bhattacharya, S. (2014) · Työväentutkimus Vuosikirja · Cited by 8

Automating Personalized Battery Management on Smartphones

Falaki, M. H. (2012) · eScholarship (California Digital Library) · Cited by 8

Personalised and Sustainable IEQ Monitoring: Use of Multi-Modal and Pervasive Technologies

Coulby, G., Clear, A. K., Jones, O., & Godfrey, A. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20064897 · Cited by 7

In situ affect detection in mobile devices: a multimodal approach for advertisement using social network

Adibuzzaman, M., Jain, N., Steinhafel, N., Haque, M., Ahmed, F., Ahamed, S. I., & Love, R. R. (2013) · SIAP · doi:10.1145/2577554.2577562 · Cited by 8

Mobile health

Varshney, U. (2014) · International Workshop on Pervasive Wireless Healthcare · doi:10.1145/2633651.2633656 · Cited by 7

Beyond sensors

Barish, G., Lester, P. E., Saltzman, W. R., & Elbogen, E. B. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2638728.2638811 · Cited by 7

Being, Having, Doing, and Interacting: A Personal Informatics Approach to Understanding Human Need Satisfaction in Everyday Life

Hoefer, M., & Voida, S. (2023) · Conference on Designing Interactive Systems · doi:10.1145/3563657.3596120 · Cited by 4

Assessing personality through objective behavioral sensing

Wang, H., & Marsella, S. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273590 · Cited by 6

Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life

Daniel, K. E., Mendu, S., Baglione, A. N., Cai, L., Teachman, B. A., Barnes, L. E., & Boukhechba, M. (2021) · Anxiety Stress & Coping · doi:10.1080/10615806.2021.1959916 · Cited by 5

Mobile Sensing Around the Globe: Considerations for Cross-Cultural ResearchPreprint

Phan, L. V., Modersitzki, N., Gloystein, K. K., & Müller, S. R. (2022) · doi:10.31234/osf.io/q8c7y · Cited by 6

Mental Health and Sensing

Tushar, A. K., Kabir, M. A., & Ahmed, S. I. (2020) · Intelligent systems reference library · doi:10.1007/978-3-030-54932-9_11 · Cited by 4

“Now, I have my baby so I don’t go anywhere”: A mixed method approach to the ‘everyday’ and young motherhood integrating qualitative interviews and passive digital data from mobile devices

Hagaman, A. K., Mercado, D. L., Poudyal, A., Bemme, D., Boone, C., van Heerden, A. C., Byanjankar, P., Maharjan, S. M., Thapa, A., & Kohrt, B. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269443 · Cited by 5

Activity recognition using a spectral entropy signature

Márquez, J. B. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370313 · Cited by 5

Measuring Cognitive Status from Speech in a Smart Home Environment

Fraser, K., & Komeili, M. (2021) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2021.9513645 · Cited by 4

Technological Advances in Clinical Assessment

Heinz, M. V., Thomas, N., Nguyen, N. D., Griffin, T. Z., & Jacobson, N. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818697-8.00171-0 · Cited by 6

Individual Decision Model for Using Technology of Health Crowdsensing in the Digital Era

Li, L., Cai, X., He, X., Yu, X., Li, H., & Liu, Y. (2022) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.302651 · Cited by 5

"Eat What You Want and Be Healthy!"

Hwang, Y., Lee, S., Jeon, H. S., Park, J. H. Y., Lee, K. W., & Lee, J. (2018) · doi:10.1145/3279954.3279958 · Cited by 5

Sensors and Sensibilities: Exploring Interactions for Habitat Comfort with An Environmental-Physiological Sensing Eyewear In the Wild

Zhong, S., Chwalek, P. C., Perry, N., Ramsay, D. B., Miller, C., Lalanne, D., Alavi, H. S., & Paradiso, J. A. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103510 · Cited by 5

Smartphone-based Qualitative Analyses of Social Activities During Family Time

Sahiti, K., Kalanadhabhatta, M., Bhunia, S., Singhal, A., & Majethia, R. (2017) · HumanSys@SenSys · doi:10.1145/3144730.3144735 · Cited by 4

Effects of Personal Characteristics on Temporal Response Patterns in Ecological Momentary Assessments

Tominaga, T., Yamamoto, S., Kurashima, T., & Toda, H. (2021) · IFIP TC13 International Conference on Human-Computer Interaction · doi:10.1007/978-3-030-85607-6_1 · Cited by 2

New mechanisms for privacy in human-in-the-loop cyber-physical systems

Halcu, I., Nunes, D., Sgârciu, V., & Silva, J. S. (2015) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2015.7340770 · Cited by 4

Towards in situ affect detection in mobile devices

Adibuzzaman, M., Jain, N., Steinhafel, N., Haque, M., Ahmed, F., Ahamed, S. I., & Love, R. R. (2013) · Research in Adaptive and Convergent Systems · doi:10.1145/2513228.2513290 · Cited by 4

AÇIK ALAN REKREASYON AKTİVİTELERİNDE DİJİTAL TEKNOLOJİLERİN KULLANIMI

Ekinci, E. (2023) · Spor ve Rekreasyon Araştırmaları Dergisi · doi:10.52272/srad.1353789 · Cited by 3

The Effects of Rhythmic Footstep and Sound Interactions on Creativity: A Design and Evaluation Study

Tan, P., & Ren, X. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2378253 · Cited by 3

Experience: Barriers and Opportunities of Wearables for Eating Research

Pedram, M., Fernandes, G. J., Romano, C., Wei, B., Sen, S., Hester, J. D., & Alshurafa, N. I. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3573841 · Cited by 3

SOK: Application of machine learning models in child and youth mental health decision-making

Daneshvar, H., Boursalie, O., Samavi, R., Doyle, T. E., Duncan, L., Pires, P., & Sassi, R. B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00003-x · Cited by 3

Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling

Mullick, T., Shaaban, S., Radović, A., & Doryab, A. (2024) · JMIR AI · doi:10.2196/47805 · Cited by 4

Estimating Physical Characteristics with Body-worn Accelerometers Based on Activity Similarities

Masuda, A., & Maekawa, T. (2016) · Journal of Information Processing · doi:10.2197/ipsjjip.24.237 · Cited by 3

Invited Speech: Data Analytics and (Interpretable) Machine Learning for Social Good

Martoglia, R. (2021) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00319 · Cited by 3

Detecting and Capitalizing on Physiological Dimensions of Psychiatric Illness

Matthews, M. L., Abdullah, S., Gay, G. K., & Choudhury, T. (2016) · International Conference on Physiological Computing Systems (PhyCS) · doi:10.5220/0005952600980104 · Cited by 3

Moving from lab testing of Active & Healthy Ageing technologies to real-life implementation

Billis, A. S., Konstantinidis, E. I., Bratsas, C., Σιούντας, Α., & Bamidis, P. D. (2016) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2910674.2935847 · Cited by 3

Shaping the Design of Smartphone-Based Interventions for Self-HarmPreprint

Bell, B. T. (2020) · doi:10.31234/osf.io/eu5pw · Cited by 3

Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

Bangamuarachchi, W., Chamantha, A., Meegahapola, L., Kim, H., Ruiz-Correa, S., Perera, I., & Gatica-Perez, D. (2023) · ACM Trans. Comput. Heal. · doi:10.1145/3723047 · Cited by 12

Analysis of Peer Group Behavior Among University Students

Kunchay, S., & Kalanadhabhatta, L. M. (2018) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3170427.3180299 · Cited by 2

User Recognition based on Gait Pattern via Smartwatch Accelerometer in Unrestricted Environment

Khan, S., Shah, S. M. A., Arsalan, A., & Ahmed, W. (2023) · International Conference on Communication, Computing and Digital Systems (C-CODE) · doi:10.1109/c-code58145.2023.10139875 · Cited by 2

A literature review on multimodal deep learning models for detecting mental disorders in conversational data: Pre-transformer and transformer-based approaches

Shao, Z. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/18/20230993 · Cited by 2

PROPOSTA DE APLICAÇÃO DE UMA PLATAFORMA DE CUIDADO HÍBRIDO PARA PACIENTES COM SINTOMA DEPRESSIVO

Netto, A. V. (2024) · REVISTA SAÚDE MULTIDISCIPLINAR · doi:10.53740/rsm.v16i1.742 · Cited by 1

Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone Sensors

Champa, A. I., Rabbi, M. F., Eishita, F. Z., & Zibran, M. F. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-55174-1_10 · Cited by 2

Clinical Perspective on Passive Audio Vocal Measurement in the Evaluation of Selective Mutism

Xu, H., Stroud, J., Jozanovic, R. K., Clucas, J. C., Son, J. J., Koo, B., Schwarz, J., Klein, A., Busman, R., & Milham, M. P. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00443 · Cited by 2

MASON

Wendt, N., & Julien, C. (2016) · IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft) · doi:10.1145/2897073.2897099 · Cited by 2

Person Identification Based on Accelerations Sensed in Smartphones with LSTM

Takahashi, Y., Nakamura, K., Kamiyama, T., Oguchi, M., & Yamaguchi, S. (2021) · Journal of Information Processing · doi:10.2197/ipsjjip.29.707 · Cited by 2

Investigation of Context-aware System Using Activity Recognition

Watanabe, Y., Suzumura, R., Matsuno, S., & Ohyama, M. (2019) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic.2019.8669035 · Cited by 2

Community and Family Medicine

Rabbi, M., Choudhury, T., Ali, S., & Berke, E. (2013)

Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study

Servia-Rodríguez, S., Rachuri, K. K., Mascolo, C., Rentfrow, P. J., Lathia, N., & Sandstrom, G. M. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052618 · Cited by 134

The Assessment of Cognitive and Physical Well-Being Through Ambient Sensor Measures of Movement Towards Longitudinal Monitoring of Activities of Daily Living

Bennett, S. L., Cohen-McFarlane, M., Wallace, B., Goubran, R. A., & Knoefel, F. D. (2021) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2021.9513640 · Cited by 8

Using smartphones to sense, assess, and improve well-being

Choudhury, T. (2014) · doi:10.1145/2611264.2611275 · Cited by 2

Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study

Servia-Rodríguez, S., Rachuri, K., Mascolo, C., Rentfrow, P., Lathia, N., & Sandstrom, G. (2017) · Apollo (University of Cambridge) · doi:10.17863/cam.8315