Cited by 356Emotion and Mood RecognitionContext-Aware Activity Recognition SystemsDigital Mental Health Interventions

The DOI matches its Crossref record; 185 of the 193 references it cites were found in a registry or an index.

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

Open access

Garcia-Ceja, Enrique, Riegler, Michael, Nordgreen, Tine, Jakobsen, Petter, Oedegaard, Ketil J., Tørresen, Jim·2018·Pervasive and Mobile Computing

DOI 10.1016/j.pmcj.2018.09.003

Open this paper in CoChat — free account Open PDF

Abstract

Personal and ubiquitous sensing technologies such as smartphones have allowed the continuous collection of data in an unobtrusive manner. Machine learning methods have been applied to continuous sensor data to predict user contextual information such as location, mood, physical activity, etc. Recently, there has been a growing interest in leveraging ubiquitous sensing technologies for mental health care applications, thus, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on. This paper surveys recent research works in mental health monitoring systems (MHMS) using sensor data and machine learning. We focused on research works about mental disorders/conditions such as: depression, anxiety, bipolar disorder, stress, etc. We propose a classification taxonomy to guide the review of related works and present the overall phases of MHMS. Moreover, research challenges in the field and future opportunities are also discussed.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.pmcj.2018.09.003

Cites

185 of 185 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
A survey on transfer learning
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Cohen, P., Cohen, P., West, S. G., & Aiken, L. S. (2014) · Psychology Press eBooks · doi:10.4324/9781410606266 · Cited by 20,870
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 12,864
World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects
Cook, R. J., Dickens, B. M., & Fathalla, M. F. (2003) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199241323.003.0025 · Cited by 10,825
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972

Show all 193 references

Research works printed but not linked: 3

R. LiKamWa, Y. Liu, N.D. Lane, L. Zhong, Can your smartphone infer your mood, in: PhoneSense workshop, 2011, pp. 1–5.
Pan, 2010, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22, 1345, 10.1109/TKDE.2009.191 · DOI · Paper page
B. Frénay, M. Verleysen, Classification in the presence of label noise: a survey 25 (5) (2014) 845–869. · DOI · Paper page

Sorted by citations

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom
Lee, E., Torous, J. B., Choudhury, M. D., Depp, C. A., Graham, S. A., Kim, H., Paulus, M. P., Krystal, J. H., & Jeste, D. V. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.02.001 · Cited by 523
A New Benchmark for Mental Health Systems: Tackling the Social and Economic Costs of Mental Ill-Health
OECD (2021) · OECD Health Policy Studies · doi:10.1787/4ed890f6-en · Cited by 407
Machine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321
Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review
Vistorte, A. O. R., Deroncele-Acosta, Á., Ayala, J. L. M., Barrasa, Á., López-Granero, C., & Martí-González, M. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1387089 · Cited by 412
Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions
Rahate, A., Walambe, R., Ramanna, S., & Kotecha, K. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.12.003 · Cited by 214
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 247
Graph Neural Networks in IoT: A Survey
Dong, G., Tang, M., Wang, Z., Gao, J., Guo, S., Cai, L., Gutiérrez, R., Campbell, B., Barnes, L. E., & Boukhechba, M. (2022) · ACM Transactions on Sensor Networks · doi:10.1145/3565973 · Cited by 226
Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Liang, P. P., Zadeh, A., & Morency, L. (2024) · ACM Computing Surveys · doi:10.1145/3656580 · Cited by 438
Digital Mental Health Challenges and the Horizon Ahead for Solutions
Balcombe, L., & Leo, D. D. (2021) · JMIR Mental Health · doi:10.2196/26811 · Cited by 212
Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions
Aleem, S., Huda, N. U., Amin, R., Khalid, S., Alshamrani, S. S., & Alshehri, A. (2022) · Electronics · doi:10.3390/electronics11071111 · Cited by 184
Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches
Khoo, L. S., McNaney, R., Lim, M. K., & McNaney, R. (2024) · Sensors · doi:10.3390/s24020348 · Cited by 194
Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling
Rykov, Y., Thach, T.-Q., Bojic, I., Christopoulos, G., & Car, J. (2021) · JMIR mHealth and uHealth · doi:10.2196/24872 · Cited by 151
Multimodal Classification: Current Landscape, Taxonomy and Future Directions
Sleeman, W. C., Kapoor, R., & Ghosh, P. (2022) · ACM Computing Surveys · doi:10.1145/3543848 · Cited by 140
A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model
Zeberga, K., Attique, M., Shah, B., Ali, F., Jembre, Y. Z., & Chung, T. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7893775 · Cited by 129
Retracted
The Lancet Commission on self-harm
Moran, P., Chandler, A., Dudgeon, P., Kirtley, O. J., Knipe, D. W., Pirkis, J., Sinyor, M., Allister, R., Ansloos, J. P., Ball, M. A., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(24)01121-8 · Cited by 143
Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses
Benoit, J. R. A., Onyeaka, H., Keshavan, M. S., & Torous, J. B. (2020) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000268 · Cited by 116
Pervasive computing in the context of COVID-19 prediction with AI-based algorithms
Magesh, S., Niveditha, V. R., Rajakumar, P. S., Rammohan, S. R., & Natrayan, L. (2020) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-07-2020-0082 · Cited by 106
Sensor, Wearable, and Remote Patient Monitoring Competencies for Clinical Care and Training: Scoping Review
Hilty, D. M., Armstrong, C. M., Edwards‐Stewart, A., Gentry, M. T., Luxton, D. D., & Krupinski, E. A. (2021) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-020-00190-3 · Cited by 111
Deep learning techniques for suicide and depression detection from online social media: A scoping review
Malhotra, A., & Jindal, R. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109713 · Cited by 102
Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review
de Moura, I. R., Teles, A. S., Viana, D., Marques, J., Coutinho, L., & Silva, F. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104278 · Cited by 98
Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review
Mendes, J. P. M., de Moura, I. R., van de Ven, P. W. J., Viana, D., Silva, F., Coutinho, L., Teixeira, S. S., Rodrigues, J. J. P. C., & Teles, A. S. (2021) · Journal of Medical Internet Research · doi:10.2196/28735 · Cited by 85
Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis
Asare, K. O., Moshe, I., Terhorst, Y., Vega, J., Hosio, S. J., Baumeister, H., Pulkki-Råbäck, L., & Ferreira, D. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101621 · Cited by 86
Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers
Ćosić, K., Popović, S., Šarlija, M., Kesedžić, I., & Jovanović, T. (2020) · Croatian Medical Journal · doi:10.3325/cmj.2020.61.279 · Cited by 87
An interpretable machine learning approach to multimodal stress detection in a simulated office environment
Naegelin, M., Weibel, R. P., Kerr, J. I., Schinazi, V. R., Marca, R. L., von Wangenheim, F., Hölscher, C., & Ferrario, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104299 · Cited by 83
Intelligent nanophotonics: when machine learning sheds light
Wu, N., Sun, Y., Hu, J., Yang, C., Bai, Z., Wang, F. X., Cui, X., He, S., Li, Y., Zhang, C., et al. (2025) · eLight · doi:10.1186/s43593-025-00085-x · Cited by 91
Machine Learning Techniques for Prediction of Mental Health
Jain, T. K., Jain, A., Hada, P. S., Kumar, H., Verma, V. K., & Patni, A. (2021) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca51532.2021.9545061 · Cited by 84
Federated Learning on Multimodal Data: A Comprehensive Survey
Lin, Y., Gao, Y., Gong, M., Zhang, S., Zhang, Y., & Li, Z. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1398-0 · Cited by 73
Speech as a Biomarker: Opportunities, Interpretability, and Challenges
Ramanarayanan, V., Lammert, A. C., Rowe, H. P., Quatieri, T. F., & Green, J. R. (2022) · Perspectives of the ASHA Special Interest Groups · doi:10.1044/2021_persp-21-00174 · Cited by 70
Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’
Birk, R. H., & Samuel, G. N. (2020) · Sociology of Health & Illness · doi:10.1111/1467-9566.13175 · Cited by 77
Influential Usage of Big Data and Artificial Intelligence in Healthcare
Yang, Y. C., Islam, S. U., Noor, A., Khan, S., Afsar, W., & Nazir, S. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5812499 · Cited by 68
“[19] have surveyed sensor data and machine learning about mental health monitoring systems.”
Background
Retracted
Smartphone based human activity monitoring and recognition using ML and DL: a comprehensive survey
Thakur, D., & Biswas, S. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01899-y · Cited by 68
A Survey of Privacy Vulnerabilities of Mobile Device Sensors
Delgado-Santos, P., Stragapede, G., Tolosana, R., Guest, R., Deravi, F., & Vera-Rodríguez, R. (2022) · ACM Computing Surveys · doi:10.1145/3510579 · Cited by 79
A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions
Hilty, D. M., Armstrong, C. M., Luxton, D. D., Gentry, M. T., & Krupinski, E. A. (2021) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-021-00199-2 · Cited by 70
A scoping review of social media in child, adolescents and young adults: research findings in depression, anxiety and other clinical challenges
Hilty, D. M., Stubbe, D. E., McKean, A. J. S., Hoffman, P. E., Zalpuri, I., Myint, M. T., Joshi, S. V., Pakyürek, M., & Li, S. T. (2023) · BJPsych Open · doi:10.1192/bjo.2023.523 · Cited by 94
Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review
Shen, S., Qi, W., Zeng, J., Shujie, L. I., Liu, X., Zhu, X., Dong, C., Wang, B., Shi, Y., Yao, J., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/77066 · Cited by 91
Analysis of Machine Learning Algorithms for Predicting Depression
Narayanrao, P. V., & Kumari, P. L. S. (2020) · doi:10.1109/iccsea49143.2020.9132963 · Cited by 61
Machine learning-based proactive social-sensor service for mental health monitoring using twitter data
Hinduja, S., Afrin, M., Mistry, S., & Krishna, A. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100113 · Cited by 61
Recent Advances in Contactless Sensing Technologies for Mental Health Monitoring
Nouman, M., Khoo, S., Mahmud, M. A. P., & Kouzani, A. Z. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3097801 · Cited by 60
Detection and classification of anxiety in university students through the application of machine learning
Bhatnagar, S., Agarwal, J., & Sharma, O. R. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.132 · Cited by 59
Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns
Jiang, Z., Seyedi, S. S., Griner, E. L., Abbasi, A., Rad, A. B., Kwon, H., Cotes, R. O., & Clifford, G. D. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3352075 · Cited by 89
Design and evaluation of a context-aware model based on psychophysiology
Bavaresco, R. S., Barbosa, J. L. V., Vianna, H. D., Büttenbender, P. C., & Dias, L. P. S. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105299 · Cited by 55
Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems
Birk, R. H., & Samuel, G. N. (2022) · Current Psychiatry Reports · doi:10.1007/s11920-022-01358-9 · Cited by 56
Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data
Borsos, B., Allaart, C. G., & van Halteren, A. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102719 · Cited by 51
Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries
Madani, S. S., Ziebert, C., Vahdatkhah, P., & Sadrnezhaad, S. K. (2024) · Batteries · doi:10.3390/batteries10060204 · Cited by 57
Prediction of Mental Health Problems among Higher Education Student Using Machine Learning
Shafiee, N. S. M., & Mutalib, S. (2020) · International Journal of Education and Management Engineering · doi:10.5815/ijeme.2020.06.01 · Cited by 57
Stress Monitoring System for Individuals with Autism Spectrum Disorders
Tomczak, M. T., Wojcikowski, M., Pankiewicz, B., Lubinski, J., Majchrowicz, J., Majchrowicz, D., Walasiewicz, A., Kilinski, T., & Szczerska, M. (2020) · IEEE Access · doi:10.1109/access.2020.3045633 · Cited by 50
Mobile Mental Health: A Review of Applications for Depression Assistance
Teles, A. S., de Moura, I. R., Viana, D., Silva, F., Coutinho, L., Endler, M., & Rabêlo, R. A. L. (2019) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2019.00143 · Cited by 48
PSYKOSE: A Motor Activity Database of Patients with Schizophrenia
Jakobsen, P., Garcia-Ceja, E., Stabell, L. A., Oedegaard, K. J., Berle, J. Ø., Thambawita, V. L., Hicks, S. A., Halvorsen, P., Fasmer, O. B., & Riegler, M. A. (2020) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms49503.2020.00064 · Cited by 47
Self-citation
An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress
Baird, A., Triantafyllopoulos, A., Zänkert, S., Ottl, S., Christ, L., Stappen, L., Konzok, J., Sturmbauer, S. C., Meßner, E.-M., Kudielka, B. M., et al. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.750284 · Cited by 55
Precision medicine and digital phenotyping: Digital medicine's way from more data to better health
Baumgartner, R. R. (2021) · Big Data & Society · doi:10.1177/20539517211066452 · Cited by 45
Visual language navigation: a survey and open challenges
Park, S., & Kim, Y. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10174-9 · Cited by 49
Toward Automatic Anxiety Detection in Autism: A Real-Time Algorithm for Detecting Physiological Arousal in the Presence of Motion
Puli, A., & Kushki, A. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2919273 · Cited by 49
Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol
Spinazze, P., Rykov, Y. G., Bottle, A., & Car, J. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-032255 · Cited by 45
A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges
Arif, M. (2021) · Journal of Information Security and Cybercrimes Research · doi:10.26735/gbtv9013 · Cited by 46
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Wang, Z., Yang, Z., Azimi, I., & Rahmani, A. M. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782516 · Cited by 42
Using Big Data-machine learning models for diabetes prediction and flight delays analytics
Thérence, N., & Laassiri, J. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00355-0 · Cited by 41
Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games
Karapapas, C., & Goumopoulos, C. (2021) · Applied Sciences · doi:10.3390/app11178184 · Cited by 40
Evaluating Multimodal Wearable Sensors for Quantifying Affective States and Depression With Neural Networks
Ahmed, A., Ramesh, J., Ganguly, S., Aburukba, R., Sagahyroon, A. A., & Aloul, F. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3303436 · Cited by 43
Mental health status monitoring for people with autism spectrum disorder using machine learning
Jayanthi, S., Priyadharshini, V., Kirithiga, V., & Premalatha, S. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01524-z · Cited by 40
Cognitive Load Monitoring With Wearables–Lessons Learned From a Machine Learning Challenge
Gjoreski, M., Mahesh, B., Kolenik, T., Garbas, J.-U., Seuß, D., Gjoreski, H., Luštrek, M., Gams, M., & Pejović, V. (2021) · IEEE Access · doi:10.1109/access.2021.3093216 · Cited by 46
PredictEYE: Personalized Time Series Model for Mental State Prediction Using Eye Tracking
Jyotsna, C., Amudha, J., Ram, A., Fruet, D., & Nollo, G. (2023) · IEEE Access · doi:10.1109/access.2023.3332762 · Cited by 38
One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression
Frogner, J. I., Noori, F. M., Halvorsen, P., Hicks, S. A., Garcia-Ceja, E., Tørresen, J., & Riegler, M. A. (2019) · doi:10.1145/3347444.3356238 · Cited by 37
Self-citation
Hybrid Machine Learning Approaches for Predicting and Diagnosing Major Depressive Disorder
Balakrishna, N., Krishnan, M. B. M., & Ganesh, D. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150363 · Cited by 35
Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients
Hennemann, S., Kuhn, S., Witthöft, M., & Jungmann, S. M. (2022) · JMIR Mental Health · doi:10.2196/32832 · Cited by 31
“The advancement of passive mobile sensing through smartphones or wearables (eg, mobility pattern, facial expression, and speech analysis [81,82]) may allow for in situ, fine-grained digital phenotyping even without this active user input.”
Background
Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models
Ariöz, U., Smrke, U., Plohl, N., & Mlakar, I. (2022) · Diagnostics · doi:10.3390/diagnostics12112683 · Cited by 37
Multimodal Classification of Anxiety Based on Physiological Signals
Vaz, M., Summavielle, T., Sebastião, R., & Ribeiro, R. P. (2023) · Applied Sciences · doi:10.3390/app13116368 · Cited by 37
User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation
Garcia-Ceja, E., Riegler, M. A., Kvernberg, A. K., & Tørresen, J. (2019) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-019-09248-1 · Cited by 35
Self-citation
Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being
Gyrard, A., Tabeau, K., Fiorini, L., Kung, A., Sengès, É., de Mul, M., Giuliani, F., Lefebvre, D., Hoshino, H., Fabbricotti, I. N., et al. (2021) · International Journal of Social Robotics · doi:10.1007/s12369-021-00821-6 · Cited by 32
Revisiting the role of computational neuroimaging in the era of integrative neuroscience
Loosen, A. M., Kato, A., & Gu, X. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01946-8 · Cited by 38
Explainable artificial intelligence systems for predicting mental health problems in autistics
Atlam, E.-S., Rokaya, M., Masud, M., Meshref, H., Alotaibi, R. S., Almars, A. M., Assiri, M., & Gad, I. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.12.120 · Cited by 43
Digital Phenotyping Using Multimodal Data
Cohen, A. S., Cox, C. R., Masucci, M. D., Le, T. P., Cowan, T., Coghill, L. M., Holmlund, T. B., & Elvevåg, B. (2020) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-020-00215-4 · Cited by 33
The Concept of Advanced Multi-Sensor Monitoring of Human Stress
Vavrinský, E., Stopjaková, V., Kopáni, M., & Kosnacova, H. (2021) · Sensors · doi:10.3390/s21103499 · Cited by 35
Activity Tracker–Based Metrics as Digital Markers of Cardiometabolic Health: Cross-Sectional Study
Rykov, Y., Thach, T.-Q., Dunleavy, G., Roberts, A. C., Christopoulos, G., Soh, C. K., & Car, J. (2020) · JMIR mHealth and uHealth · doi:10.2196/16409 · Cited by 29
A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques
Atzeni, D., Bacciu, D., Mazzei, D., & Prencipe, G. (2022) · Sensors · doi:10.3390/s22134925 · Cited by 32
Adaptive Systems for Internet-Delivered Psychological Treatments
Mukhiya, S. K., Wake, J. D., İnal, Y., & Lamo, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3002793 · Cited by 31
HYPERAKTIV
Hicks, S. A., Stautland, A., Fasmer, O. B., Førland, W., Hammer, H. L., Halvorsen, P., Mjeldheim, K., Oedegaard, K. J., Osnes, B., Syrstad, V. E. G., et al. (2021) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3458305.3478454 · Cited by 30
Self-citation
Mental Health Analysis of Employees using Machine Learning Techniques
Sujal, B. H., Neelima, K., Chandrasekaran, D., Bhuvanashree, P., Duraipandian, K., Rajan, S., & Sathiyanarayanan, M. (2022) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets53615.2022.9668526 · Cited by 31
3D printed flexible wearable sensors based on triply periodic minimal surface structures for biomonitoring applications
Imanian, M. E., Kardan-Halvaei, M., Nasrollahi, F., Imanian, A., Montazerian, H., & Nasrollahi, V. (2022) · Smart Materials and Structures · doi:10.1088/1361-665x/aca6bc · Cited by 30
QCLR: Quantum-LSTM contrastive learning framework for continuous mental health monitoring
Padha, A., & Sahoo, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121921 · Cited by 29
Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders
Singh, J., & Hamid, M. A. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10042-2 · Cited by 28
A Critical Review of Multimodal-multisensor Analytics for Anxiety Assessment
Senaratne, H., Oviatt, S. L., Ellis, K., & Melvin, G. A. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3556980 · Cited by 31
An experience of the cognitive map-based classifier usage in astronaut's emotional state monitoring
Korovin, I., Klimenko, A., Kalyaev, I., & Борисовна, С. И. (2021) · Acta Astronautica · doi:10.1016/j.actaastro.2021.01.022 · Cited by 26
High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study
Zhou, W., Chan, Y. E., Foo, C.-S., Zhang, J., Teo, J. X., Dávila, S., Huang, W., Yap, J. J. L., Cook, S. A., Tan, P. B. O., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/34669 · Cited by 26
Wearable Technology for Signal Acquisition and Interactive Feedback in Autism Spectrum Disorder Intervention: A Review
Gao, X., Yin, L., Tian, S., Huang, Y., & Ji, Q. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3374762 · Cited by 32
FedMEKT: Distillation-based embedding knowledge transfer for multimodal federated learning
Le, H. Q., Nguyen, M. N. H., Thwal, C. M., Qiao, Y., Zhang, C., & Hong, C. S. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.107017 · Cited by 32
Real-time mental stress detection technique using neural networks towards a wearable health monitor
Mukherjee, N., Mukhopadhyay, S., & Gupta, R. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac3aae · Cited by 26
Automated detection of mental disorders using physiological signals and machine learning: A systematic review and scientometric analysis
Singh, J., & Sharma, D. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17504-1 · Cited by 28
ADAM-sense: Anxiety-displaying activities recognition by motion sensors
Khan, N. S., Ghani, M. S., & Anjum, G. (2021) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2021.101485 · Cited by 24
Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health
Gyrard, A., & Boudaoud, K. (2022) · Applied Sciences · doi:10.3390/app12199712 · Cited by 24
Multimodal Technologies for Remote Assessment of Neurological and Mental Health
Ramanarayanan, V. (2024) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-24-00142 · Cited by 32
Electrospun multifunctional nanofibers for advanced wearable sensors
Tian, Y., Wang, J., Chen, H., Lin, H., Wu, S., Zhang, Y., Tian, M., Meng, J., Saeed, W., Liu, W., et al. (2024) · Talanta · doi:10.1016/j.talanta.2024.127085 · Cited by 24
Mental health ubiquitous monitoring supported by social situation awareness: A systematic review
de Moura, I. R., Teles, A. S., Silva, F., Viana, D., Coutinho, L., Barros, F., & Endler, M. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103454 · Cited by 25
Accurate Identification of ADHD among Adults Using Real-Time Activity Data
Kaur, A., & Kahlon, K. S. (2022) · Brain Sciences · doi:10.3390/brainsci12070831 · Cited by 23
A Personalised Emotion-Based Model for Relaxation in Virtual Reality
Heyse, J., Vega, M. T., Jonge, T. D., Backere, F. D., & Turck, F. D. (2020) · Applied Sciences · doi:10.3390/app10176124 · Cited by 26
An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia
Price, G. D., Heinz, M. V., Zhao, D. Y., Nemesure, M. D., Ruan, F., & Jacobson, N. C. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.08.013 · Cited by 31
Predicting mental health using social media: A roadmap for future development
Safa, R., Edalatpanah, S. A., & Sorourkhah, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00014-x · Cited by 28
Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being
Rahmani, A. M., Lai, J., Jafarlou, S., Azimi, I., Yunusova, A., Rivera, A. P., Labbaf, S., Anzanpour, A., Dutt, N. D., Jain, R., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.933587 · Cited by 26
Multimodal Sensing-Enabled Large Language Models for Automated Emotional Regulation: A Review of Current Technologies, Opportunities, and Challenges
Yu, L., Ge, Y., Ansari, S., Imran, M. A., & Ahmad, W. (2025) · Sensors · doi:10.3390/s25154763 · Cited by 26
Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder
Jakobsen, P., Stautland, A., Riegler, M. A., Côté‐Allard, U., Sepasdar, Z., Nordgreen, T., Tørresen, J., Fasmer, O. B., & Oedegaard, K. J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0262232 · Cited by 21
Self-citation
A CNN-Based Framework for Predicting Public Emotion and Multi-Level Behaviors Based on Network Public Opinion
Lin, H., & Bu, N. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.909439 · Cited by 23

Authors: Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J.
Affiliations: University of Oslo (NO)
Haukeland University Hospital (NO)
Corresponding author: Garcia-Ceja, Enrique
ORCID: Garcia-Ceja, Enrique
Riegler, Michael
Nordgreen, Tine
Jakobsen, Petter
Oedegaard, Ketil J.
Tørresen, Jim
Venue: Pervasive and Mobile Computing
Publisher: Elsevier BV
Published: 2018-12
Volume: 51
Pages: 1-26
ISSN: 1574-1192 (ISSN-L)
DOI: 10.1016/j.pmcj.2018.09.003
OpenAlex: W2890929258
Cited by: 356
References: 193
Citations by year: 2026: 23 · 2025: 53 · 2024: 62 · 2023: 60 · 2022: 70 · 2021: 47 · 2020: 27 · 2019: 14
FWCI: 28.82
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Pervasive and Mobile Computing (publishedVersion, cc-by-nc-nd)
Funders: Norwegian Research Council (259293/o70)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords: mental health monitoring, ubiquitous sensing, smartphones, multimodal sensing, depression detection, anxiety detection, stress detection, bipolar disorder, continuous monitoring, classification taxonomy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks