The DOI matches its Crossref record; 43 of the 45 references it cites were found in a registry or an index.
Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health
Open accessAbstract
While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.
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“For instance, StudentLife [63] combined sensing and self-reports to assess the impact of student workload on stress, whereas Snapshot [160] tracked their mood and sleep.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [160]
In the citing paper
“Some recent works employ recurrent neural networks [20], feed-forward layers [159], multi-task learning [160] and autoencoders to fill in missing sensor data [161], or to learn better representations [162].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [160]
In the citing paper
“In addition to group-level fairness, individual fairness arises in several tasks where the target manifests itself differently in distinct (human) subjects. This ranges from the expression of fatigue in runners (54), to stress and emotion in voice (55, 56), and, crucially, mood prediction from wearable sensors (46).”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [46]
In the citing paper
“Our findings also corroborate the results of Taylor et al. (46), who report that personalized Multitask Learning (shared common layers neural network, multi-kernel learning using support vector machines, and hierarchical Bayesian models with a common Dirichlet process prior) improved prediction accuracies by 11–21% over non-personalized models.”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [46]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
(2014) · Cited by 33,419
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
(1999) · Cited by 7,995
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093
Psychological Stress and Susceptibility to the Common Cold
Cohen, S., Tyrrell, D. A. J., & Smith, A. P. (1991) · New England Journal of Medicine · doi:10.1056/nejm199108293250903 · Cited by 1,712
Temperament, personality, and the mood and anxiety disorders.
Clark, L. A., Watson, D., & Mineka, S. (1994) · Journal of Abnormal Psychology · doi:10.1037/0021-843x.103.1.103 · Cited by 1,685
Veenhoven, R. (2008) · Journal of Happiness Studies · doi:10.1007/s10902-006-9042-1 · Cited by 771
The Evidence Framework Applied to Classification Networks
Mackay, D. (1992) · Neural Computation · doi:10.1162/neco.1992.4.5.720 · Cited by 733
Multi-task learning for classification with dirichlet process priors
Xue, Y., Liao, X., Carin, L., Krishnapuram, B., & Bartlett, P. (2007) · Cited by 478
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
A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling
Baxter, J. (1997) · Machine Learning · doi:10.1023/a:1007327622663 · Cited by 460
MoodScope: building a mood sensor from smartphone usage patterns
LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013) · Proceeding of the 11th annual international conference on Mobile systems, applications, and services · doi:10.1145/2462456.2464449 · Cited by 444
To transfer or not to transfer
Rosenstein, M. T. (2005) · Cited by 429
Personality, self-esteem, and demographic predictions of happiness and depression
Cheng, H., & Furnham, A. (2003) · Personality and Individual Differences · doi:10.1016/s0191-8869(02)00078-8 · Cited by 413
Spark: The Revolutionary New Science of Exercise and the Brain
Ratey, J. J., & Hagerman, E. (2008) · Medical Entomology and Zoology · Cited by 394
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
Does the perception that stress affects health matter? The association with health and mortality.
Keller, A., Litzelman, K., Wisk, L. E., Maddox, T., Cheng, E. R., Creswell, P. D., & Witt, W. P. (2012) · Health Psychology · doi:10.1037/a0026743 · Cited by 375
Automatic identification of artifacts in electrodermal activity data
Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318762 · Cited by 279
Self-citationMulti-task reinforcement learning
Wilson, A., Fern, A., Ray, S., & Tadepalli, P. V. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273624 · Cited by 262
Learning with Hierarchical-Deep Models
Salakhutdinov, R., Tenenbaum, J. B., & Torralba, A. B. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.269 · Cited by 229
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
Improving multiview face detection with multi-task deep convolutional neural networks
Zhang, C., & Zhang, Z. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6835990 · Cited by 210
Spark: The Revolutionary New Science of Exercise and the Brain
Hobart, M. (2008) · Psychiatric Services · doi:10.1176/appi.ps.59.8.939 · Cited by 205
BOWER, B., BYLSMA, L. M., MORRIS, B. H., & ROTTENBERG, J. (2010) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2009.00816.x · Cited by 192
Detecting Work Stress in Offices by Combining Unobtrusive Sensors
Koldijk, S., Neerincx, M. A., & Kraaij, W. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2610975 · Cited by 184
A Multi-Task Learning Framework for Emotion Recognition Using 2D Continuous Space
Xia, R., & Liu, Y. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2512598 · Cited by 181
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
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
Self-citationCome rain or come shine: Individual differences in how weather affects mood.
Klimstra, T. A., Frijns, T., Keijsers, L., Denissen, J. J. A., Raaijmakers, Q. A. W., van Aken, M. A. G., Koot, H. M., van Lier, P. A. C., & Meeus, W. (2011) · Emotion · doi:10.1037/a0024649 · Cited by 101
MoodScope: Building a Mood Sensor from Smartphone Usage Patterns
LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2012) · Rice Research Repository (Rice University) · Cited by 97
Zhang, B., Provost, E. M., & Essl, G. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2684799 · Cited by 79
Neural network multitask learning for traffic flow forecasting
Jin, F., & Sun, S. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634057 · Cited by 70
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
New Methods for Stress Assessment and Monitoring at the Workplace
Carneiro, D. R., Nováis, P., Augusto, J. C., & Payne, N. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2699633 · Cited by 66
Aichele, S., Rabbitt, P., & Ghisletta, P. (2016) · Psychological Science · doi:10.1177/0956797615626906 · Cited by 39
Li, J., Wang, X., & Hovy, E. H. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2662090 · Cited by 37
Multi-task and multi-view learning of user state
Kandemir, M., Vetek, A., Gönen, M., Arto Klami, A., & Samuel Kaski, A. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.02.057 · Cited by 36
Busk, J., Faurholt‐Jepsen, M., Frost, M. M., Bardram, J. E., Kessing, L. V., & Winther, O. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00867-6 · Cited by 26
Dopamine and circadian rhythms in seasonal affective disorder
Partonen, T. (1996) · Medical Hypotheses · doi:10.1016/s0306-9877(96)90079-1 · Cited by 17
Personality Factors in Stress and Anxiety
Brebner, J. (2018) · Taylor & Francis eBooks · doi:10.4324/9781315825724-2 · Cited by 7
Flagged first, then by citations
2 of the 45 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- jaques, 2015, Multi-task, multi-kernel learning for estimating individual wellbeing, Proc 28th Int Conf Neural Inf Process Syst Workshop Multimodal ML, 898
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Sorted by citations
Self-Supervised ECG Representation Learning for Emotion Recognition
Sarkar, P., & Etemad, A. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3014842 · Cited by 411
Personalized machine learning for robot perception of affect and engagement in autism therapy
Rudovic, O., Lee, J., Dai, M., Schuller, B., & Picard, R. W. (2018) · Science Robotics · doi:10.1126/scirobotics.aao6760 · Cited by 316
Self-citationWearable-Based Affect Recognition—A Review
Schmidt, P., Reiss, A., Dürichen, R., & Laerhoven, K. V. (2019) · Sensors · doi:10.3390/s19194079 · Cited by 291
EntropyHub: An open-source toolkit for entropic time series analysis
Flood, M. W., & Grimm, B. P. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259448 · Cited by 136
Behavioral and Physiological Signals-Based Deep Multimodal Approach for Mobile Emotion Recognition
Yang, K., Wang, C., Gu, Y., Sarsenbayeva, Z., Tag, B., Dingler, T., Wadley, G., & Gonçalves, J. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3100868 · Cited by 115
Metadata
- Authors
- Taylor, S., Jaques, N., Nosakhare, E., Sano, A., & Picard, R.
- Affiliations
- MIT Media Lab (US)
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2020-04-01
- Volume
- 11
- Issue
- 2
- Pages
- 200-213
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- PMC
- PMC7266106
- OpenAlex
- W2779582454
- Cited by
- 251
- References
- 45
- Citations by year
- 2026: 15 · 2025: 24 · 2024: 37 · 2023: 36 · 2022: 55 · 2021: 30 · 2020: 28
- FWCI
- 18.74
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Transactions on Affective Computing (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- MIT Media Lab Consortium
- National Institutes of Health (R01GM105018)
- Samsung
- NEC Corporation
- Natural Sciences and Engineering Research Council of Canada
- 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 › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- mood prediction, stress prediction, health prediction, multi-task learning, personalized machine learning, individual differences, Bayesian hierarchical model, Dirichlet process, wearable sensors, meteorological observations, well-being
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