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

The PHQ-9: Validity of a brief depression severity measure

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910

The validity of the Hospital Anxiety and Depression Scale

Bjelland, I., Dahl, A. A., Haug, T. T., & Neckelmann, D. (2002) · Journal of Psychosomatic Research · doi:10.1016/s0022-3999(01)00296-3 · Cited by 9,779

The PHQ-9: A New Depression Diagnostic and Severity Measure

Kroenke, K., & Spitzer, R. L. (2002) · Psychiatric Annals · doi:10.3928/0048-5713-20020901-06 · Cited by 5,898 · cited 4×

A Practical Guide to Support Vector Classication

Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056

The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review

Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2010) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2010.03.006 · Cited by 4,057

Computational social science

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Limits of Predictability in Human Mobility

Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234

A survey of mobile phone sensing

Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5560598 · Cited by 2,292

The economic cost of brain disorders in Europe

Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H., & Jönsson, B. (2012) · European Journal of Neurology · doi:10.1111/j.1468-1331.2011.03590.x · Cited by 1,560

Department of Computer Science

Buisman, H., Gomez-rodriguez, M., & Hassanpour, S. (1993) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(93)90083-n · Cited by 1,212

State affect recognition using smartphone sensing data

Cai, L., Boukhechba, M., Wu, C., Chow, P. I., Teachman, B. A., Barnes, L. E., & Gerber, M. S. (2018) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3278576.3284386 · Cited by 20

"I Just Can't Help But Smile Sometimes": Collaborative Self-Management of Depression

Burgess, E. R., Reddy, M. C., & Mohr, D. C. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512917 · Cited by 20

Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring

Mehrotra, A., & Musolesi, M. (2017) · doi:10.1145/3089341.3089342 · Cited by 19

Can We Assess Mental Health Through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation

Tsakalidis, A., Liakata, M., Damoulas, T., & Cristea, A. I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10997-4_25 · Cited by 19

Insights: Anwendungsmöglichkeiten von passivem Smartphone-Tracking im therapeutischen Kontext

Meßner, E.-M., Sariyska, R., Mayer, B., Montag, C., Kannen, C., Schwerdtfeger, A. R., & Baumeister, H. (2019) · Verhaltenstherapie · doi:10.1159/000501735 · Cited by 19

Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study

Wang, Y., Ren, X., Liu, X., & Zhu, T. (2021) · JMIR mhealth and uhealth · doi:10.2196/19046 · Cited by 23

Healthwalks: sensing fine-grained individual health condition via mobility data

Lin, Z., Lyu, S., Cao, H., Xu, F., Wei, Y., Samet, H., & Li, Y. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432229 · Cited by 18

Physiological Stress Level Estimation Based on Smartphone Logs

Yamamoto, N., Ochiai, K., Inagaki, A., Fukazawa, Y., Kimoto, M., Kiriu, K., Kaminishi, K., Ota, J., Okimura, T., Terasawa, Y., et al. (2018) · International Conference on Mobile Computing and Ubiquitous Network (ICMU) · doi:10.23919/icmu.2018.8653590 · Cited by 18

Role of AI/ML in the study of mental health problems of the students: a bibliometric study

Rajkishan, S. S., Meitei, A. J., & Singh, A. (2023) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-02052-6 · 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

Assessing mental health issues on college campuses

Tseng, V. W.-S., Merrill, M., Wittleder, F., Abdullah, S., Aung, M. H., & Choudhury, T. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968308 · Cited by 18

Assessing Mental Stress Based on Smartphone Sensing Data: An Empirical Study

Wang, F., Wang, Y., Wang, J., Xiong, H., Zhao, J., & Zhang, D. (2019) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00200 · Cited by 18

A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning

Ferreira, D. L., Nunes, B. A. A., Campos, C. A. V., & Obraczka, K. (2020) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3380970 · Cited by 19

An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients

Cai, L., Boukhechba, M., Gerber, M. S., Barnes, L. E., Showalter, S. L., Cohn, W. F., & Chow, P. I. (2019) · Smart Health · doi:10.1016/j.smhl.2019.100086 · Cited by 19

Insights – future implications of passive smartphone sensing in the therapeutic context

Messner, E.-M., Sariyska, R., Mayer, B., Montag, C., Kannen, C., Schwerdtfeger, A., & Baumeister, H. (2022) · Verhaltenstherapie · doi:10.1159/000501951 · Cited by 17

Application of Improved SMOTE and XGBoost Algorithm in the Analysis of Psychological Stress Test for College Students

Du, W. (2022) · Journal of Electrical and Computer Engineering · doi:10.1155/2022/2760986 · Cited by 17

Automatic depression screening using social interaction data on smartphones

Ware, S., Yue, C., Morillo, R., Shang, C., Bi, J., Kamath, J., Russell, A. C., Song, D., Bamis, A., & Wang, B. (2022) · Smart Health · doi:10.1016/j.smhl.2022.100356 · Cited by 19

Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach

Akbarova, S., Im, M., Kim, S. H., Toshnazarov, K. E., Chung, K., Chun, J., Noh, Y., & Kim, Y.-A. (2023) · Sensors · doi:10.3390/s23218866 · Cited by 19

Assessing Sleep Quality Using Mobile EMAs: Opportunities, Practical Consideration, and Challenges

Lim, J., Jeong, C. Y., Lim, J. M., Chung, S., Kim, G., Noh, K. J., & Jeong, H. (2022) · IEEE Access · doi:10.1109/access.2021.3140074 · Cited by 18

A systems biology approach to the digital behaviorome

Rashidisabet, H., Thomas, P. J., Ajilore, O., Zulueta, J., Moore, R. C., & Leow, A. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.07.003 · Cited by 16

Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care

Bauer, J. F., Gerczuk, M., Schindler‐Gmelch, L., Amiriparian, S., Ebert, D. D., Krajewski, J., Schuller, B. W., & Berking, M. (2024) · Depression and Anxiety · doi:10.1155/2024/9667377 · Cited by 17

Early Detection of Depression and Treatment Response Prediction using Machine Learning: A Review

Kharel, P., Sharma, K., Dhimal, S., & Sharma, S. (2019) · International Conference on Advanced Computational and Communication Paradigms (ICACCP) · doi:10.1109/icaccp.2019.8882891 · Cited by 16

Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices

Jiang, J., Chao, Z., Bertozzi, A. L., Wang, W. W., Young, S. D., & Needell, D. M. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357831 · Cited by 16

Measuring Membership Privacy on Aggregate Location Time-Series

Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2020) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3392154 · Cited by 19

Attentional Multi-graph Convolutional Network for Regional Economy Prediction with Open Migration Data

Xu, F., Li, Y., & Xu, S. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403273 · Cited by 16

Anonymous or Not? Understanding the Factors Affecting Personal Mobile Data Disclosure

Perentis, C., Vescovi, M., Leonardi, C., Moiso, C., Musolesi, M., Pianesi, F., & Lepri, B. (2017) · ACM Transactions on Internet Technology · doi:10.1145/3017431 · Cited by 17

“Trust Us”

Bati, G. F., & Singh, V. K. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173904 · Cited by 17

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Chatterjee, S., Mishra, J., Sundram, F., & Roop, P. (2023) · Sensors · doi:10.3390/s24010164 · Cited by 15

Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping

Webb, C. A., Ren, B., Rahimi-Eichi, H., Gillis, B. W., Chung, Y., & Baker, J. T. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03394-4 · Cited by 15

Depression Anatomy Using Combinational Deep Neural Network

Rustagi, A., Manchanda, C., Sharma, N., & Kaushik, I. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5148-2_3 · Cited by 15

A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity

Ross, M. K., Tulabandhula, T., Bennett, C. C., Baek, E., Kim, D., Hussain, F., Demos, A. P., Ning, E., Langenecker, S. A., Ajilore, O., et al. (2023) · Sensors · doi:10.3390/s23031585 · Cited by 16

Measuring Affective State: Subject-Dependent and -Independent Prediction Based on Longitudinal Multimodal Sensing

Berkemeier, L., Kamphuis, W., Brouwer, A.-M., de Vries, H., Schadd, M. P. D., van Baardewijk, J. U., Oldenhuis, H. K. E., Verdaasdonk, R. M., & van Gemert‐Pijnen, J. E. W. C. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3474098 · Cited by 15

DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling

Busso, M., Bontempelli, A., Malcotti, L. J., Meegahapola, L., Kun, P., Diwakar, S., Nutakki, C. S., Britez, M. D. R., Xu, H., Song, D., et al. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3712289 · Cited by 14

Mobile User Research

Consolvo, S., Bentley, F. R., Hekler, E. B., & Phatak, S. S. (2017) · Synthesis lectures on mobile and pervasive computing · doi:10.2200/s00763ed1v01y201703mpc012 · Cited by 14

Combining heterogeneous user generated data to sense well-being

Tsakalidis, A., Liakata, M., Damoulas, T., Jellinek, B., Guo, W., & Cristea, A. I. (2016) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33727657 · Cited by 14

Quantifying the Relationships between Everyday Objects and Emotional States through Deep Learning Based Image Analysis Using Smartphones

Darvariu, V.-A., Convertino, L., Mehrotra, A., & Musolesi, M. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3380997 · Cited by 14

Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients

Dong, G., Boukhechba, M., Shaffer, K. M., Ritterband, L. M., Gioeli, D. G., Reilley, M. J., Le, T. M., Kunk, P. R., Bauer, T. W., & Chow, P. I. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-021-00098-4 · Cited by 14

Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation

Xia, Z., Chikersal, P., Venkatesh, S., Walker, E., Dey, A. K., & Goel, M. (2025) · Journal of Medical Internet Research · doi:10.2196/70871 · Cited by 13

"Ultimately We're Together": Understanding New Parents' Experiences of Co-parenting

Lin, Y.-F., Li, N., Huang, W.-H., Ecsedy, K., Feinberg, M. E., Teti, D. M., & Carroll, J. M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687018 · Cited by 13

Nomophobia, Psychopathology, and Smartphone-Inferred Behaviors in Youth With Depression: Longitudinal Study

Zhang, T., Benavides, A. F. C., Schmaal, L., Kostakos, V., & D’Alfonso, S. (2025) · JMIR Formative Research · doi:10.2196/57512 · Cited by 14

Affect Estimation with Wearable Sensors

Yan, S., Hosseinmardi, H., Kao, H.-T., Narayanan, S. S., Lerman, K., & Ferrara, E. (2020) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-019-00066-z · Cited by 13

Understanding practices and needs of researchers in human state modeling by passive mobile sensing

Xu, X., Mankoff, J. C., & Dey, A. K. (2021) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-021-00072-4 · Cited by 13

Human mobility variations in response to restriction policies during the COVID-19 pandemic: An analysis from the Virus Watch community cohort in England, UK

Cheng, T., Chen, T., Liu, Y., Aldridge, R. W., Nguyen, V. G., Hayward, A. C., & Michie, S. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.999521 · Cited by 13

Unimodal vs. Multimodal Prediction of Antenatal Depression from Smartphone-based Survey Data in a Longitudinal Study

Zhong, M., van Zoest, V., Bilal, A.-M., Papadopoulos, F. C., & Castellano, G. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536221.3556605 · Cited by 14

Exploring the association between mobility behaviours and academic performances of students: a context-aware traj-graph (CTG) analysis

Ghosh, S., & Ghosh, S. K. (2018) · Progress in Artificial Intelligence · doi:10.1007/s13748-018-0164-6 · Cited by 13

GANs for Privacy-Aware Mobility Modeling

Fontana, I., Langheinrich, M., & Gjoreski, M. (2023) · IEEE Access · doi:10.1109/access.2023.3260981 · Cited by 12

Discovery and recognition of unknown activities

Ye, J., Fang, L., & Dobson, S. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968288 · Cited by 12

Estimating Individualized Daily Self-Reported Affect with Wearable Sensors

Yan, S., Hosseinmardi, H., Kao, H.-T., Narayanan, S. S., Lerman, K., & Ferrara, E. (2019) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2019.8904691 · Cited by 12

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

Having a Bad Day? Detecting the Impact of Atypical Events Using Wearable Sensors

Burghardt, K., Tavabi, N., Ferrara, E., Narayanan, S. S., & Lerman, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80387-2_25 · Cited by 12

Towards pervasive geospatial affect perception

Raja, M., Exler, A., Hemminki, S., Konomi, S., Sigg, S., & Inoue, S. (2017) · GeoInformatica · doi:10.1007/s10707-017-0294-1 · Cited by 12

Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing

Vaid, S. S., & Harari, G. M. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4_5 · Cited by 12

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

Supporting Real‐Time Contextual Inquiry through Sensor Data

Gorkovenko, K., BURNETT, D., Murray-Rust, D., Thorp, J. K., & Richards, D. (2019) · Ethnographic Praxis in Industry Conference Proceedings · doi:10.1111/1559-8918.2019.01307 · Cited by 12

A survey of autonomous monitoring systems in mental health

Gopalakrishnan, A., Gururajan, R., Zhou, X., Venkataraman, R., Chan, K. C., & Higgins, N. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1527 · Cited by 11

Clustering and Feature Analysis of Smartphone Data for Depression Monitoring

Nguyen, B., Kolappan, S., Bhat, V., & Krishnan, S. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629737 · Cited by 11

EarlyScreen

Kalanadhabhatta, M., Santana, A. M., Zhang, Z., Ganesan, D., Grabell, A. S., & Rahman, T. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534583 · Cited by 11

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

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

Characterizing and Removing Oscillations in Mobile Phone Location Data

Katsikouli, P., Fiore, M., Furno, A., & Stanica, R. (2019) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2019.8793034 · Cited by 11

Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data

Shende, C., Sahoo, S., Sam, S., Patel, P., Morillo, R., Wang, X., Ware, S., Bi, J., Kamath, J., Russell, A. C., et al. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3610932 · Cited by 10

Urban Knowledge Graph Aided Mobile User Profiling

Liu, Y., Zhou, Z., Li, Y., & Jin, D. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3596604 · Cited by 10

An Extensible Pervasive Platform for Large-Scale Anticipatory Mobile Computing

Meurisch, C., Jeutter, B., Schmidt, W., Gundling, N., Schmidt, B., Herrlich, F., & Mühlhäuser, M. (2017) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2017.54 · Cited by 10

Change-point detection in hierarchical circadian models

Moreno-Muñoz, P., Ramírez, D., & Artés-Rodrı́guez, A. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107820 · Cited by 10

Measuring Membership Privacy on Aggregate Location Time-Series

Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2020) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/3393691.3394200 · Cited by 12

Passive Health Monitoring Using Large Scale Mobility Data

Zhang, Y., Xu, F., Li, T., Kostakos, V., Hui, P., & Li, Y. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448078 · Cited by 10

SmokingOpp

Chatterjee, S., Moreno, A., Lizotte, S. L., Akther, S., Ertin, E., Fagundes, C. P., Lam, C. Y., Rehg, J. M., Wan, N., Wetter, D. W., et al. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3380987 · Cited by 10

Bodeum

Song, S., Yamashita, N., & Kim, J. (2020) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3421937.3421973 · Cited by 9

Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study

Terhorst, Y., Meßner, E.-M., Asare, K. O., Montag, C., Kannen, C., & Baumeister, H. (2024) · Journal of Medical Internet Research · doi:10.2196/55308 · Cited by 10

Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study

Lee, T.-Y., Chen, C., Chen, I., Chen, H., Liu, C., Wu, S., Hsiao, C. K., & Kuo, P. (2024) · Journal of Medical Internet Research · doi:10.2196/55635 · Cited by 10

Graph attention networks unveil determinants of intra- and inter-city health disparity

Liu, C., Fan, C. H., & Mostafavi, A. (2024) · Urban Informatics · doi:10.1007/s44212-024-00049-5 · Cited by 9

GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling

Xu, X., Liu, X., Zhang, H., Wang, W., Nepal, S. K., Sefidgar, Y. S., Seo, W., Kuehn, K. S., Huckins, J. F., Morris, M. E., et al. (2024) · GetMobile Mobile Computing and Communications · doi:10.1145/3686138.3686147 · Cited by 9

The Synergy of Clinical Psychology and Affective Computing: Advancements in Emotion Recognition and Therapy

Joshi, R., & Jadeja, M. (2024) · The Springer series in applied machine learning · doi:10.1007/978-3-031-63821-3_2 · Cited by 9

Emotion-Aware Systems for Promoting Human Well-being

Lascio, E. D. (2018) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3267305.3267316 · Cited by 9

On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic based on Pre-COVID-19 On-Campus Colocation

Wang, W., Wu, J., Nepal, S. K., DaSilva, A. W., Hedlund, E. L., Murphy, E. I., Rogers, C., & Huckins, J. F. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3479888 · Cited by 9

Exploring Post COVID-19 Outbreak Intradaily Mobility Pattern Change in College Students: A GPS-Focused Smartphone Sensing Study

Wu, C., Fritz, H., Miller, M., Craddock, C., Kinney, K. A., Castelli, D. M., & Schnyer, D. M. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.765972 · Cited by 9

An exploration in remote blood pressure management: Application of daily routine pattern based on mobile data in health management

Xiang, Y., Li, S., & Zhang, P. (2021) · Fundamental Research · doi:10.1016/j.fmre.2021.11.006 · Cited by 9

Usability Testing of a Gratitude Application for Promoting Mental Well-Being

Alqahtani, F., Alslaity, A., & Orji, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05412-9_21 · Cited by 8

Profiling mobility patterns and driving behaviors of individual drivers via trajectory trait

Liu, Y., Gui, Z., Xu, Y., Gao, S., Zhao, A., Meng, F., Peng, D., Li, F., del Bo, L., Wu, H., et al. (2024) · The Innovation Geoscience · doi:10.59717/j.xinn-geo.2024.100114 · Cited by 8

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

Unobtrusive Monitoring of Parkinson's Disease Based on Digital Biomarkers of Human Behaviour

Vega, J., Jay, C., Vigo, M., & Harper, S. (2017) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3132525.3134782 · Cited by 8

Machine Learning for Phone-Based Relationship Estimation

Liu, T., Nicholas, J., Theilig, M.-M., Guntuku, S. C., Kording, K. P., Mohr, D. C., & Ungar, L. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369820 · Cited by 11

Flexibility Versus Routineness in Multimodal Health Indicators: A Sensor-based Longitudinal in Situ Study of Information Workers

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