MoodScope: building a mood sensor from smartphone usage patterns

Development and validation of brief measures of positive and negative affect: The PANAS scales.

Watson, D., Clark, L. A., & Tellegen, A. (1988) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.54.6.1063 · Cited by 32,433

Untitled work

(1996) · Cited by 20,179

A circumplex model of affect.

Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334

Journal of personality and social psychology

Armstrong, J. (1988) · International Journal of Forecasting · doi:10.1016/0169-2070(88)90126-4 · Cited by 10,544

Affective Computing

Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382

Facial action coding system

Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019

Review of personality and social psychology

Davies, M. (1983) · Personality and Individual Differences · doi:10.1016/0191-8869(83)90072-7 · Cited by 3,394

The class imbalance problem: A systematic study1

Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295

The Positive and Negative Affect Schedule (PANAS): construct validity, measurement properties and normative data in a large non-clinical sample.

Crawford, J. R., & Henry, J. D. (2004) · British Journal of Clinical Psychology · doi:10.1348/0144665031752934 · Cited by 2,957

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797

Psychological targeting as an effective approach to digital mass persuasion

Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710966114 · Cited by 892

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

Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802

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

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

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

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

Emotion recognition using wireless signals

Zhao, M., Adib, F., & Katabi, D. (2016) · Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking · doi:10.1145/2973750.2973762 · Cited by 461

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

Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., & Lukowicz, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2343154 · Cited by 384

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

Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.09.003 · Cited by 356

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

Bota, P. J., Wang, C., Fred, A. L. N., & Silva, H. (2019) · IEEE Access · doi:10.1109/access.2019.2944001 · Cited by 348

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

Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016) · PeerJ · doi:10.7717/peerj.2537 · Cited by 313

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

Nunes, D., Zhang, P., & Silva, J. S. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2398816 · Cited by 295

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

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

Taylor, S., Jaques, N., Nosakhare, E., Sano, A., & Picard, R. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2784832 · Cited by 251

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

Pielot, M., Dingler, T., Pedro, J. S., & Oliver, N. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2804252 · Cited by 258

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

Sano, A., Phillips, A. J., Yu, A. Z., McHill, A. W., Taylor, S., Jaques, N., Czeisler, C. A., Klerman, E. B., & Picard, R. W. (2015) · 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2015.7299420 · Cited by 236

Learning from the ubiquitous language

Lü, X., Ai, W., Liu, X., Li, Q., Wang, N., Huang, G., & Mei, Q. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971724 · Cited by 222

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

Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654933 · Cited by 217

Defining the Sensor Society

Andrejevic, M., & Burdon, M. (2015) · Television & New Media · doi:10.1177/1527476414541552 · Cited by 218

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

Cho, C.-H., Lee, T., Kim, M.-G., In, H. P., Kim, L., & Lee, H.-J. (2019) · Journal of Medical Internet Research · doi:10.2196/11029 · Cited by 183

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

Asselbergs, J., Ruwaard, J., Ejdys, M., Schrader, N., Sijbrandij, M., & Riper, H. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5505 · Cited by 192

Emc: Emotion-aware mobile cloud computing in 5G

Chen, M., Zhang, Y., Li, Y., Mao, S., & Leung, V. C. M. (2015) · IEEE Network · doi:10.1109/mnet.2015.7064900 · Cited by 179

Using Big Data as a window into consumers’ psychology

Matz, S., & Netzer, O. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.05.009 · Cited by 182

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

Wearable Devices for Precision Medicine and Health State Monitoring

Jeong, I. C., Bychkov, D., & Searson, P. C. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2871638 · Cited by 155

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

Seneviratne, S., Seneviratne, A. P., Mohapatra, P., & Mahanti, A. (2014) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/2636242.2636244 · Cited by 150

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

Jaques, N., Taylor, S., Azaria, A., Ghandeharioun, A., Sano, A., & Picard, R. W. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344575 · Cited by 150

Through a Gender Lens

Chen, Z., Lü, X., Ai, W., Li, H., Mei, Q., & Liu, X. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186157 · Cited by 164

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

Fulford, D., Campellone, T. R., & Gard, D. E. (2018) · Clinical Psychology Review · doi:10.1016/j.cpr.2018.05.007 · Cited by 143

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

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

Ethical perspectives on recommending digital technology for patients with mental illness

Bauer, M., Glenn, T., Monteith, S., Bauer, R., Whybrow, P. C., & Geddes, J. R. (2017) · International Journal of Bipolar Disorders · doi:10.1186/s40345-017-0073-9 · Cited by 137

Identifying Indicators of Smartphone Addiction Through User-App Interaction

Noë, B., Turner, L. D., Linden, D. E. J., Allen, S. M., Winkens, B., & Whitaker, R. M. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.04.023 · Cited by 150

Privacy in the age of psychological targeting

Matz, S., Appel, R. E., & Kosiński, M. (2019) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2019.08.010 · Cited by 137

Exploiting Mobile Big Data: Sources, Features, and Applications

Cheng, X., Fang, L., Hong, X., & Yang, L. (2017) · IEEE Network · doi:10.1109/mnet.2017.1500295nm · Cited by 123

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

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

Rickard, N., Arjmand, H.-A., Bakker, D., & Seabrook, E. (2016) · JMIR Mental Health · doi:10.2196/mental.6202 · Cited by 118

How smartphone usage correlates with social anxiety and loneliness

Gao, Y., Li, A., Zhu, T., Liu, X., & Liu, X. (2016) · PeerJ · doi:10.7717/peerj.2197 · Cited by 111

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

Lepri, B., Staiano, J., Sangokoya, D., Letouzé, E., & Oliver, N. (2017) · Studies in Big Data · doi:10.1007/978-3-319-54024-5_1 · Cited by 111

HealthyOffice: Mood recognition at work using smartphones and wearable sensors

Zenonos, A., Khan, A., Kalogridis, G., Vatsikas, S., Lewis, T., & Sooriyabandara, M. (2016) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2016.7457166 · Cited by 118

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

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 107

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

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 98

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

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

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 96

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 90

Argument-based mixed recommenders and their application to movie suggestion

Briguez, C. E., Budán, M. C. D., Deagustini, C. A. D., Maguitman, A. G., Capobianco, M., & Simari, G. R. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.03.046 · Cited by 86

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

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

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 85

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

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

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 80

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 107

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 73

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 74

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

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

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

Kołakowska, A., Szwoch, W., & Szwoch, M. (2020) · Sensors · doi:10.3390/s20216367 · Cited by 76

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 71

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 72

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

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

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

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

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 69

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

Epstein, D. H., Tyburski, M., Kowalczyk, W. J., Burgess-Hull, A. J., Phillips, K. A., Curtis, B. L., & Preston, K. L. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0234-6 · Cited by 64

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

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 66

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 69

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

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

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

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

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

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

Desmet, P. (2015) · Data Archiving and Networked Services (DANS) · Cited by 58

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

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

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

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 70

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

Pervasive stress recognition for sustainable living

Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (2014) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2014.6815230 · Cited by 55

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 57

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

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

Memento

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

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

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 54

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 51

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

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

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

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 52

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

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

Theorizing Drones and Droning Theory

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

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

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

Towards emotion recognition from contextual information using machine learning

o, M., Rodríguez, L.-F., & Gutiérrez-García, J. O. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01485-x · Cited by 43

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

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

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 30

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

Mobile Sensing at the Service of Mental Well-being

Servia-Rodríguez, S., Rachuri, K., Cecilia Mascolo, C., Rentfrow, P., Lathia, N., & Sandstrom, G. (2017) · Cited by 5