MoodScope: building a mood sensor from smartphone usage patterns

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Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones

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Enabling large-scale human activity inference on smartphones using community similarity networks (csn)

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SpeakerSense: Energy Efficient Unobtrusive Speaker Identification on Mobile Phones

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Towards unobtrusive emotion recognition for affective social communication

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Foundations of Human Computing: Facial Expression and Emotion

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A study of mobile mood awareness and communication through MobiMood

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Towards Mood Based Mobile Services and Applications

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Studying Affect Among the Chinese: The Circular Way

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Psychological targeting as an effective approach to digital mass persuasion

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Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

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Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges

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Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis

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Emotion recognition using wireless signals

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Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients

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Mental health monitoring with multimodal sensing and machine learning: A survey

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A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals

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The relationship between mobile phone location sensor data and depressive symptom severity

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A Survey on Human-in-the-Loop Applications Towards an Internet of All

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Smartphone sensing methods for studying behavior in everyday life

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Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

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When attention is not scarce - detecting boredom from mobile phone usage

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Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

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Learning from the ubiquitous language

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Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study

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Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

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Defining the Sensor Society

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Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study.

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Emc: Emotion-aware mobile cloud computing in 5G

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Using Big Data as a window into consumers’ psychology

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DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks

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Wearable Devices for Precision Medicine and Health State Monitoring

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Predicting user traits from a snapshot of apps installed on a smartphone

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Predicting students' happiness from physiology, phone, mobility, and behavioral data

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Through a Gender Lens

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Social motivation in schizophrenia: How research on basic reward processes informs and limits our understanding

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

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Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Kim, H., Lee, S., Lee, S., Hong, S., Kang, H., & Kim, N. (2019) · JMIR mHealth and uHealth · doi:10.2196/14149 · Cited by 131 · see citing works

Ethical perspectives on recommending digital technology for patients with mental illness

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Identifying Indicators of Smartphone Addiction Through User-App Interaction

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Privacy in the age of psychological targeting

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Exploiting Mobile Big Data: Sources, Features, and Applications

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EMMA: an emotion-aware wellbeing chatbot

Ghandeharioun, A., McDuff, D., Czerwinski, M., & Rowan, K. (2019) · 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2019.8925455 · Cited by 126 · see citing works

Development of a Mobile Phone App to Support Self-Monitoring of Emotional Well-Being: A Mental Health Digital Innovation

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How smartphone usage correlates with social anxiety and loneliness

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The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

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HealthyOffice: Mood recognition at work using smartphones and wearable sensors

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A new aspect on P2P online lending default prediction using meta-level phone usage data in China

Ma, L., Zhao, X., Zhou, Z., & Liu, Y. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.05.001 · Cited by 107 · see citing works

DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones

Boukhechba, M., Daros, A. R., Fua, K., Chow, P. I., Teachman, B. A., & Barnes, L. E. (2018) · Smart Health · doi:10.1016/j.smhl.2018.07.005 · Cited by 105 · see citing works

MoodExplorer

Zhang, X., Li, W., Chen, X., & Lu, S. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3161414 · Cited by 100 · see citing works

Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder

Gideon, J., Provost, E. M., & McInnis, M. G. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472099 · Cited by 95 · see citing works

Student Perspectives on Digital Phenotyping: The Acceptability of Using Smartphone Data to Assess Mental Health

Rooksby, J., Morrison, A., & Murray-Rust, D. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300655 · Cited by 87 · see citing works

A Survey on Compressed Sensing in Vehicular Infotainment Systems

Guo, J., Song, B., He, Y., Yu, F. R., & Sookhak, M. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2705027 · Cited by 87 · see citing works

Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study

Boukhechba, M., Chow, P. I., Fua, K., Teachman, B. A., & Barnes, L. E. (2018) · JMIR Mental Health · doi:10.2196/10101 · Cited by 87 · see citing works

Mobile Big Data: The Fuel for Data-Driven Wireless

Cheng, X., Fang, L., Yang, L., & Cui, S. R. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2714189 · Cited by 87 · see citing works

Argument-based mixed recommenders and their application to movie suggestion

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The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing

Lind, M. N., Byrne, M. L., Wicks, G., Smidt, A. M., & Allen, N. B. (2018) · JMIR Mental Health · doi:10.2196/10334 · Cited by 84 · see citing works

Individuals’ Stress Assessment Using Human-Smartphone Interaction Analysis

Ciman, M., & Wac, K. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2592504 · Cited by 82 · see citing works

Using Big Data to study subjective well-being

Luhmann, M. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.006 · Cited by 81 · see citing works

Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study (Preprint)

Cao, J., Truong, A. L., Banu, S., Shah, A. A., Sabharwal, A., & Moukaddam, N. (2019) · doi:10.2196/preprints.14045 · Cited by 81 · see citing works

Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction

Mehrotra, A., Hendley, R., & Musolesi, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968299 · Cited by 77 · see citing works

A survey on mobile affective computing

Politou, E., Alepis, E., & Patsakis, C. (2017) · Computer Science Review · doi:10.1016/j.cosrev.2017.07.002 · Cited by 77 · see citing works

Worker Well-Being: What it Is, and how it Should Be Measured

Wijngaards, I., King, O. C., Burger, M., & van Exel, J. N. A. (2021) · Applied Research in Quality of Life · doi:10.1007/s11482-021-09930-w · Cited by 88 · see citing works

MyTraces

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 71 · see citing works

Sensing, Understanding, and Shaping Social Behavior

Shmueli, E., Singh, V. K., Lepri, B., & Pentland, A. (2014) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2014.2307438 · Cited by 73 · see citing works

Automated Tracking Approaches for Studying Online Media Use: A Critical Review and Recommendations

Christner, C., Urman, A., Adam, S., & Maier, M. (2021) · Communication Methods and Measures · doi:10.1080/19312458.2021.1907841 · Cited by 73 · see citing works

Movement patterns in women at risk for perinatal depression: use of a mood-monitoring mobile application in pregnancy

Faherty, L. J., Hantsoo, L., Appleby, D., Sammel, M. D., Bennett, I. M., & Wiebe, D. J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx005 · Cited by 62 · see citing works

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors

Kołakowska, A., Szwoch, W., & Szwoch, M. (2020) · Italian National Conference on Sensors · doi:10.3390/s20216367 · Cited by 66 · see citing works

Monitoring social anxiety from mobility and communication patterns

Boukhechba, M., Huang, Y., Chow, P. I., Fua, K., Teachman, B. A., & Barnes, L. E. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3125607 · Cited by 70 · see citing works

Oxytocin receptors (OXTR) and early parental care: An interaction that modulates psychiatric disorders

Cataldo, I., Azhari, A., Lepri, B., & Esposito, G. (2017) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2017.10.007 · Cited by 70 · see citing works

MiLift: Efficient Smartwatch-Based Workout Tracking Using Automatic Segmentation

Shen, C., Ho, B.-J., & Srivastava, M. B. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2775641 · Cited by 69 · see citing works

Stress modelling and prediction in presence of scarce data

Maxhuni, A., Hernandez-Leal, P., Sucar, L., Osmani, V., Morales, E., & Mayora, O. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.08.023 · Cited by 61 · see citing works

Happiness Recognition from Mobile Phone Data

Bogomolov, A., Lepri, B., & Pianesi, F. (2013) · 2013 International Conference on Social Computing · doi:10.1109/socialcom.2013.118 · Cited by 67 · see citing works

Meaningless comparisons lead to false optimism in medical machine learning

DeMasi, O., Kording, K., & Recht, B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184604 · Cited by 63 · see citing works

Investigating the Relationships between Mobility Behaviours and Indicators of Subjective Well–Being Using Smartphone–Based Experience Sampling and GPS Tracking

Müller, S. R., Peters, H., Matz, S., Wang, W., & Harari, G. M. (2020) · European Journal of Personality · doi:10.1002/per.2262 · Cited by 63 · see citing works

Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data

Epstein, D., Tyburski, M., Kowalczyk, W., Burgess-Hull, A., Phillips, K. A., Curtis, B. L., & Preston, K. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0234-6 · Cited by 54 · see citing works

More with less

Xu, L., Hao, X., Lane, N. D., Liu, X., & Moscibroda, T. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2807523 · Cited by 60 · see citing works

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach

Sükei, E., Norbury, A., Pérez-Rodríguez, M. M., Olmos, P. M., & Artés-Rodrı́guez, A. (2021) · JMIR mhealth and uhealth · doi:10.2196/24465 · Cited by 62 · see citing works

User profiling from their use of smartphone applications: A survey

Zhao, S., Li, S., Ramos, J., Luo, Z., Jiang, Z., Dey, A. K., & Pan, G. (2019) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2019.101052 · Cited by 63 · see citing works

IAMHAPPY: Towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness

Gyrard, A., & Sheth, A. (2020) · Smart Health · doi:10.1016/j.smhl.2019.100083 · Cited by 48 · see citing works

LEO: scheduling sensor inference algorithms across heterogeneous mobile processors and network resources

Georgiev, P., Lane, N. D., Rachuri, K. K., & Mascolo, C. (2016) · Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking · doi:10.1145/2973750.2973777 · Cited by 58 · see citing works

Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach.

Busk, J., Faurholt-Jepsen, M., Frost, M., Bardram, J. E., Vedel Kessing, L., & Winther, O. (2020) · JMIR mhealth and uhealth · doi:10.2196/15028 · Cited by 58 · see citing works

Why would you do that? predicting the uses and gratifications behind smartphone-usage behaviors

Hiniker, A., Patel, S., Kohno, T., & Kientz, J. A. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971762 · Cited by 57 · see citing works

Emotion detection from touch interactions during text entry on smartphones

Ghosh, S., Hiware, K., Ganguly, N., Mitra, B., & De, P. (2019) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2019.04.005 · Cited by 58 · see citing works

Design for Mood: Twenty Activity-Based Opportunities to Design for Mood Regulation

Desmet, P. (2015) · Data Archiving and Networked Services (DANS) · Cited by 57 · see citing works

Classifying Depression in Imbalanced Datasets Using an Autoencoder- Based Anomaly Detection Approach

Gerych, W., Agu, E., & Rundensteiner, E. A. (2019) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icosc.2019.8665535 · Cited by 58 · see citing works

SenseHAR

Jeyakumar, J. V., Lai, L., Suda, N., & Srivastava, M. B. (2019) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3356250.3360032 · Cited by 56 · see citing works

Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation

Jaques, N., Rudovic, O. O., Taylor, S., Sano, A., & Picard, R. W. (2017) · International Joint Conference on Artificial Intelligence · Cited by 56 · see citing works

Capturing the College Experience

Nepal, S. K., Liu, W., Pillai, A., Wang, W., Vojdanovski, V., Huckins, J. F., Rogers, C., Meyer, M. L., & Campbell, A. T. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3643501 · Cited by 58 · see citing works

Mobile Network Data for Public Health: Opportunities and Challenges

Oliver, N., Matic, A., & Frı́as-Martı́nez, E. (2015) · Frontiers in Public Health · doi:10.3389/fpubh.2015.00189 · Cited by 53 · see citing works

Pervasive stress recognition for sustainable living

Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (2014) · 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS) · doi:10.1109/percomw.2014.6815230 · Cited by 53 · see citing works

Using Autoencoders to Automatically Extract Mobility Features for Predicting Depressive States

Mehrotra, A., & Musolesi, M. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3264937 · Cited by 54 · see citing works

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 54 · see citing works

EngageMon

Huynh, S., Kim, S., Ko, J., Balan, R. K., & Lee, Y. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3191745 · Cited by 50 · see citing works

Memento

Jiang, S., Li, Z., Zhou, P., & Li, M. (2019) · ACM Transactions on Sensor Networks · doi:10.1145/3281630 · Cited by 53 · see citing works

Low Bandwidth Offload for Mobile AR

Jain, P. K., Manweiler, J., & Choudhury, R. R. (2016) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/2999572.2999587 · Cited by 52 · see citing works

What data are smartphone users willing to share with researchers?

Beierle, F., Tran, V. T., Allemand, M., Neff, P. K. A., Schlee, W., Probst, T., Zimmermann, J., & Pryss, R. C. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01355-6 · Cited by 53 · see citing works

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

Hung, G. C., Yang, P.-C., Chang, C.-C., Chiang, J.-H., & Chen, Y. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5551 · Cited by 49 · see citing works

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 53 · see citing works

Context Data Categories and Privacy Model for Mobile Data Collection Apps

Beierle, F., Tran, V. T., Allemand, M., Neff, P., Schlee, W., Probst, T., Pryss, R., & Zimmermann, J. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.07.139 · Cited by 50 · see citing works

Personalised depression forecasting using mobile sensor data and ecological momentary assessment

Kathan, A., Harrer, M., Küster, L., Triantafyllopoulos, A., He, X., Milling, M., Gerczuk, M., Yan, T., Rajamani, S. T., Heber, E., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.964582 · Cited by 47 · see citing works

mHealth Technologies Towards Parkinson's Disease Detection and Monitoring in Daily Life: A Comprehensive Review

Zhang, H., Song, C., Rathore, A. S., Huang, M.-C., Zhang, Y., & Xu, W. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2991813 · Cited by 48 · see citing works

TapSense

Ghosh, S., Ganguly, N., Mitra, B., & De, P. (2017) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3098279.3098564 · Cited by 47 · see citing works

The role of mobile phone technology in understanding and preventing suicidal behavior

de Beurs, D., Kirtley, O., Kerkhof, A., Portzky, G., & O’Connor, R. C. (2015) · Crisis · doi:10.1027/0227-5910/a000316 · Cited by 47 · see citing works

Theorizing Drones and Droning Theory

Andrejevic, M. (2015) · Springer eBooks · doi:10.1007/978-3-319-23760-2_2 · Cited by 45 · see citing works

Engagement-aware computing

Mathur, A., Lane, N. D., & Kawsar, F. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971760 · Cited by 44 · see citing works

Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors

Barlacchi, G., Perentis, C., Mehrotra, A., Musolesi, M., & Lepri, B. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0124-6 · Cited by 43 · see citing works

Towards emotion recognition from contextual information using machine learning

Ortega, M. G. S., Rodríguez, L.-F., & Gutierrez-Garcia, J. O. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01485-x · Cited by 39 · see citing works

Stress Detection with Deep Learning Approaches Using Physiological Signals

Albertetti, F., Simalastar, A., & Rizzotti-Kaddouri, A. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-69963-5_7 · Cited by 41 · see citing works

MoodScope: Building a Mood Sensor from Smartphone Usage Patterns

(2015)

General Terms

LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2012)

A Review on Mood Assessment Using Smartphones

Sarsenbayeva, Z., Fleming, C., Tag, B., Withana, A. I., van Berkel, N., & McEwan, A. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42283-6_22 · Cited by 5 · see citing works

How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition

Bachmann, A., Klebsattel, C., Budde, M., Riedel, T., Beigl, M., Reichert, M., Santangelo, P., & Ebner‐Priemer, U. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2804394 · Cited by 28 · see citing works

Towards Mood Based Mobile Services and Applications

Gluhak, A., Presser, M. A., Zhu, L., Esfandiyari, S., & Kupschick, S. (2007) · Springer eBooks · doi:10.1007/978-3-540-75696-5_10 · Cited by 32 · see citing works