Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data
Open accessAbstract
Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers.
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
Cites
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?
Recent work explored fine-grained mood prediction through regression and clustering, as well as novel multi-dimensional formulations of the Affect Grid through multi-task learning.
“Instead, recent work explored fine-grained mood prediction through regression and clustering [163], as well as novel multi-dimensional formulations of the Affect Grid through multi-task learning [164].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [164]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
A very brief measure of the Big-Five personality domains
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003) · Journal of Research in Personality · doi:10.1016/s0092-6566(03)00046-1 · Cited by 8,307 · see citing works
An Overview of Multi-Task Learning in Deep Neural NetworksPreprint
Ruder, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05098 · Cited by 2,409 · see citing works
Affect Grid: A single-item scale of pleasure and arousal.
Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.57.3.493 · Cited by 1,951 · see citing works
Unsupervised Learning of Video Representations using LSTMs
Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861 · see citing works
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2632048.2632054 · Cited by 1,116 · see citing works
Veenhoven, R. (2008) · Journal of Happiness Studies · doi:10.1007/s10902-006-9042-1 · Cited by 771 · see citing works
The relation between valence and arousal in subjective experience.
Kuppens, P., Tuerlinckx, F., Russell, J. A., & Barrett, L. F. (2013) · Psychological Bulletin · doi:10.1037/a0030811 · Cited by 583 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing
Lane, N., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., Doryab, A., Berke, E., Choudhury, T., & Campbell, A. (2011) · Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2011.246161 · Cited by 409 · see citing works
Data-driven advice for applying machine learning to bioinformatics problems
Olson, R. S., Cava, W. L., Mustahsan, Z., Varik, A., & Moore, J. H. (2018) · Biocomputing 2018 · doi:10.1142/9789813235533_0018 · Cited by 287 · see citing works
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 · see citing works
Geukes, K., Nestler, S., Hutteman, R., Küfner, A. C. P., & Back, M. D. (2017) · Journal of Research in Personality · doi:10.1016/j.jrp.2016.06.003 · Cited by 210 · see citing works
Improving Multi-Step Prediction of Learned Time Series Models
Venkatraman, A., Hebert, M., & Bagnell, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9590 · Cited by 198 · see citing works
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 · see citing works
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
Cao, B., Zheng, L., Zhang, C., Yu, P. S., Piscitello, A., Zulueta, J., Ajilore, O., Ryan, K., & Leow, A. D. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098086 · Cited by 145 · see citing works
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 · see citing works
Self-citationIndividual differences in the relationship between pleasure and arousal
Kuppens, P. (2007) · Journal of Research in Personality · doi:10.1016/j.jrp.2007.10.007 · Cited by 124 · see citing works
Areni, C. S., & Burger, M. (2008) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.2008.00353.x · Cited by 41 · see citing works
Aichele, S., Rabbitt, P., & Ghisletta, P. (2016) · Psychological Science · doi:10.1177/0956797615626906 · Cited by 39 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406 · see citing works
Sultana, M., Al-Jefri, M., & Lee, J. (2020) · JMIR mHealth and uHealth · doi:10.2196/17818 · Cited by 46 · see citing works
Self-supervised transfer learning of physiological representations from free-living wearable data
Spathis, D., Perez-Pozuelo, I., Brage, S., Wareham, N. J., & Mascolo, C. (2021) · Proceedings of the Conference on Health, Inference, and Learning · doi:10.1145/3450439.3451863 · Cited by 40 · see citing works
Self-citationHuman-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
Self-citation
- Authors
- Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J.
- Affiliations
- University of Cambridge (GB)
- University of Cambridge, Cambridge, United Kingdom
- University of Southampton (GB)
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 2886-2894
- OpenAlex
- W2949366704
- Cited by
- 30
- References
- 23
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 5 · 2021: 6 · 2020: 8 · 2019: 2
- FWCI
- 6.60
- Type
- conference paper
- Language
- en
- Open access
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Apollo
- Funders
- Nokia Bell Labs
- Engineering and Physical Sciences Research Council (EP/I032673/1, EP/N509620/1)
- Embiricos Trust, Jesus College, Cambridge
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- self-reported mood, multi-task learning, user heterogeneity, mood prediction
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
