{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:00:14Z","timestamp":1776128414174,"version":"3.50.1"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62303496"],"award-info":[{"award-number":["62303496"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62573441"],"award-info":[{"award-number":["62573441"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2025A1515011729"],"award-info":[{"award-number":["2025A1515011729"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.patcog.2026.113481","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T16:59:35Z","timestamp":1773421175000},"page":"113481","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Semi-supervised topic-guided contrastive learning with data augmentation for daily stress prediction using smartphone sensors"],"prefix":"10.1016","volume":"178","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8871-6198","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7116-1286","authenticated-orcid":false,"given":"Zeju","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6613-2225","authenticated-orcid":false,"given":"Zhijun","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1749-2975","authenticated-orcid":false,"given":"Zhen","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7992-7965","authenticated-orcid":false,"given":"Zhiguo","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2821-9357","authenticated-orcid":false,"given":"Changhong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.patcog.2026.113481_bib0001","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1002\/da.22778","article-title":"Severe role impairment associated with mental disorders: results of the WHO world mental health surveys international college student project","volume":"35","author":"Alonso","year":"2018","journal-title":"Depress Anxiety"},{"key":"10.1016\/j.patcog.2026.113481_bib0002","doi-asserted-by":"crossref","unstructured":"F. Wang, J. Yang, F. Pan, J. A. Bourgeois, J. H. Huang, Community series in early life stress and depression, Volume II, Frontiers in Psychiatry, 16 (2025) 1408329.","DOI":"10.3389\/fpsyt.2025.1408329"},{"issue":"9","key":"10.1016\/j.patcog.2026.113481_bib0003","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1016\/j.psyneuen.2013.02.004","article-title":"Good stress, bad stress and oxidative stress: insights from anticipatory cortisol reactivity","volume":"38","author":"Aschbacher","year":"2013","journal-title":"Psychoneuroendocrinology"},{"issue":"3","key":"10.1016\/j.patcog.2026.113481_bib0004","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/S0006-3223(03)00420-7","article-title":"Social and economic burden of mood disorders","volume":"54","author":"Simon","year":"2003","journal-title":"Biol. Psychiatry"},{"issue":"3","key":"10.1016\/j.patcog.2026.113481_bib0005","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1037\/prj0000130","article-title":"Next-generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health","volume":"38","author":"Ben-Zeev","year":"2015","journal-title":"Psychiatr. Rehabil. J."},{"issue":"1","key":"10.1016\/j.patcog.2026.113481_bib0006","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TAFFC.2016.2592504","article-title":"Individuals\u2019 stress assessment using human-smartphone interaction analysis","volume":"9","author":"Ciman","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.patcog.2026.113481_bib0007","series-title":"Observational Measurement of Behavior","author":"Yoder","year":"2010"},{"issue":"9","key":"10.1016\/j.patcog.2026.113481_bib0008","first-page":"426","article-title":"Semi-supervised deep learning for stress prediction: a review and novel solutions","volume":"12","author":"Alshamrani","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.patcog.2026.113481_bib0009","unstructured":"N. Khan, N. Sarkar, Semi-supervised generative adversarial network for stress detection using partially labeled physiological data, (2022), arXiv preprint arXiv: 2206.14976."},{"issue":"8","key":"10.1016\/j.patcog.2026.113481_bib0010","doi-asserted-by":"crossref","first-page":"3944","DOI":"10.3390\/s23083944","article-title":"Graph-based self-training for semi-supervised deep similarity learning","volume":"23","author":"Wang","year":"2023","journal-title":"Sensors"},{"issue":"9","key":"10.1016\/j.patcog.2026.113481_bib0011","doi-asserted-by":"crossref","first-page":"8934","DOI":"10.1109\/TKDE.2022.3220219","article-title":"A survey on deep semi-supervised learning","volume":"35","author":"Yang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"8","key":"10.1016\/j.patcog.2026.113481_bib0012","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1007\/s40747-025-01970-1","article-title":"Multi-behavior aware recommendation with joint contrastive learning and reinforced negative sampling","volume":"11","author":"Du","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.patcog.2026.113481_bib0013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pmcj.2018.09.003","article-title":"Mental health monitoring with multimodal sensing and machine learning: a survey","volume":"51","author":"Garcia-Ceja","year":"2018","journal-title":"Pervasive Mob. Comput."},{"key":"10.1016\/j.patcog.2026.113481_bib0014","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1016\/j.procs.2019.09.221","article-title":"Prediction of stress levels with LSTM and passive mobile sensors","volume":"159","author":"Acikmese","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.patcog.2026.113481_bib0015","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.patcog.2018.12.026","article-title":"Time series feature learning with labeled and unlabeled data","volume":"89","author":"Wang","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113481_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112793","article-title":"Deep semi-supervised relation preserving learning model","volume":"173","author":"Tian","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113481_bib0017","series-title":"2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)","first-page":"1","article-title":"EST: integrating explainable AI and semi-supervised ensemble learning for social media stress detection","author":"Tasnia","year":"2024"},{"issue":"2","key":"10.1016\/j.patcog.2026.113481_bib0018","first-page":"1","article-title":"Semi-supervised learning for wearable-based momentary stress detection in the wild","volume":"7","author":"Yu","year":"2023","journal-title":"Proc. ACM Interact. Mob. Wearable and Ubiquitous Technol."},{"issue":"1","key":"10.1016\/j.patcog.2026.113481_bib0019","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TETCI.2024.3400885","article-title":"Semi-supervised contrastive learning for time series classification in healthcare","volume":"9","author":"Liu","year":"2024","journal-title":"IEEE Trans. Emerging Top. Comput. Intell."},{"key":"10.1016\/j.patcog.2026.113481_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110874","article-title":"CARLA: self-supervised contrastive representation learning for time series anomaly detection","volume":"157","author":"Darban","year":"2025","journal-title":"Pattern Recognit."},{"issue":"15","key":"10.1016\/j.patcog.2026.113481_bib0021","doi-asserted-by":"crossref","first-page":"25041","DOI":"10.1109\/JSEN.2024.3412397","article-title":"Virtual fusion with contrastive learning for single sensor-based activity recognition","volume":"24","author":"Nguyen","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.patcog.2026.113481_bib0022","series-title":"International Conference on Machine Learning","first-page":"5606","article-title":"Clocs: contrastive learning of cardiac signals across space, time, and patients","author":"Kiyasseh","year":"2021"},{"key":"10.1016\/j.patcog.2026.113481_bib0023","series-title":"AAAI Conference on Artificial Intelligence","article-title":"TS2Vec: towards universal representation of time series","author":"Yue","year":"2021"},{"key":"10.1016\/j.patcog.2026.113481_bib0024","series-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21","first-page":"2352","article-title":"Time-series representation learning via temporal and contextual contrasting","author":"Eldele","year":"2021"},{"key":"10.1016\/j.patcog.2026.113481_bib0025","series-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21","first-page":"4653","article-title":"Time series data augmentation for deep learning: a survey","author":"Wen","year":"2021"},{"key":"10.1016\/j.patcog.2026.113481_bib0026","series-title":"International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.patcog.2026.113481_bib0027","first-page":"3285","article-title":"Looking beyond single images for contrastive semantic segmentation learning","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113481_bib0028","doi-asserted-by":"crossref","first-page":"7264","DOI":"10.1109\/TIP.2022.3221290","article-title":"Spice: semantic pseudo-labeling for image clustering","volume":"31","author":"Niu","year":"2022","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"10.1016\/j.patcog.2026.113481_bib0029","doi-asserted-by":"crossref","first-page":"15604","DOI":"10.1109\/TPAMI.2023.3308189","article-title":"Self-supervised contrastive representation learning for semi-supervised time-series classification","volume":"45","author":"Eldele","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.1016\/j.patcog.2026.113481_bib0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3550316","article-title":"Cocoa: cross modality contrastive learning for sensor data","volume":"6","author":"Deldari","year":"2022","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"issue":"2","key":"10.1016\/j.patcog.2026.113481_bib0031","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1109\/TAFFC.2024.3471654","article-title":"A topic-guided self-attention network for daily mental wellbeing prediction using mobile devices","volume":"16","author":"Xu","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.patcog.2026.113481_bib0032","series-title":"International Conference on Learning Representations","article-title":"Autoencoding variational inference for topic models","author":"Srivastava","year":"2017"},{"key":"10.1016\/j.patcog.2026.113481_bib0033","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"132","article-title":"Deep clustering for unsupervised learning of visual features","author":"Caron","year":"2018"},{"issue":"1-2","key":"10.1016\/j.patcog.2026.113481_bib0034","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0095-4470(95)80042-5","article-title":"Time series analysis of jitter","volume":"23","author":"Schoentgen","year":"1995","journal-title":"J. Phon."},{"key":"10.1016\/j.patcog.2026.113481_bib0035","series-title":"2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT)","first-page":"191","article-title":"A time series classification method for behaviour-based dropout prediction","author":"Haiyang","year":"2018"},{"key":"10.1016\/j.patcog.2026.113481_bib0036","series-title":"2020 25th International Conference on Pattern Recognition (ICPR)","first-page":"3558","article-title":"Time series data augmentation for neural networks by time warping with a discriminative teacher","author":"Iwana","year":"2021"},{"key":"10.1016\/j.patcog.2026.113481_bib0037","series-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","first-page":"1857","article-title":"Improved deep metric learning with multi-class n-pair loss objective","volume":"29","author":"Sohn","year":"2016"},{"key":"10.1016\/j.patcog.2026.113481_bib0038","first-page":"15908","article-title":"Transformer in transformer","volume":"34","author":"Han","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.patcog.2026.113481_bib0039","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1037\/prj0000243","article-title":"CrossCheck: integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse","volume":"40","author":"Ben-Zeev","year":"2017","journal-title":"Psychiatr. Rehabil. J."},{"key":"10.1016\/j.patcog.2026.113481_bib0040","first-page":"24655","article-title":"GLOBEM dataset: multi-year datasets for longitudinal human behavior modeling generalization","volume":"35","author":"Xu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"10","key":"10.1016\/j.patcog.2026.113481_bib0041","doi-asserted-by":"crossref","first-page":"9145","DOI":"10.1007\/s12652-020-02616-5","article-title":"Predicting mental health using smart-phone usage and sensor data","volume":"12","author":"Thakur","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"10.1016\/j.patcog.2026.113481_bib0042","series-title":"ICML","first-page":"3","article-title":"A new analysis of co-training","volume":"2","author":"Wang","year":"2010"},{"key":"10.1016\/j.patcog.2026.113481_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110176","article-title":"A comprehensive survey on design and application of autoencoder in deep learning","volume":"138","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"10.1016\/j.patcog.2026.113481_bib0044","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1561\/2200000056","article-title":"An introduction to variational autoencoders","volume":"12","author":"Kingma","year":"2019","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"10.1016\/j.patcog.2026.113481_bib0045","first-page":"21271","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume":"33","author":"Grill","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113481_bib0046","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"6845","article-title":"TapNet: multivariate time series classification with attentional prototypical network","volume":"34","author":"Zhang","year":"2020"},{"key":"10.1016\/j.patcog.2026.113481_bib0047","series-title":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"3545","article-title":"Semi-supervised time series classification by temporal relation prediction","author":"Fan","year":"2021"},{"key":"10.1016\/j.patcog.2026.113481_bib0048","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"14079","article-title":"Diffusion language-shapelets for semi-supervised time-series classification","volume":"38","author":"Liu","year":"2024"},{"key":"10.1016\/j.patcog.2026.113481_bib0049","series-title":"International Conference on Learning Representations","article-title":"Understanding deep learning requires rethinking generalization","author":"Zhang","year":"2017"},{"key":"10.1016\/j.patcog.2026.113481_bib0050","series-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","first-page":"7290","article-title":"JointMatch: a unified approach for diverse and collaborative pseudo-labeling to semi-supervised text classification","author":"Zou","year":"2023"},{"key":"10.1016\/j.patcog.2026.113481_bib0051","series-title":"Workshop on Challenges in Representation Learning, ICML","first-page":"896","article-title":"Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks","volume":"3","author":"Lee","year":"2013"},{"issue":"6","key":"10.1016\/j.patcog.2026.113481_bib0052","doi-asserted-by":"crossref","first-page":"6350","DOI":"10.1109\/JSEN.2023.3241410","article-title":"Daily posture behavior patterns derived from multitime-scale topic models using wearable triaxial acceleration for assessment of concern about falling","volume":"23","author":"Wang","year":"2023","journal-title":"IEEE Sens. J."},{"issue":"3","key":"10.1016\/j.patcog.2026.113481_bib0053","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10994-024-06656-2","article-title":"Dual-view data augmentation at subgraph level and graph contrastive learning for sequential recommendation","volume":"114","author":"Mu","year":"2025","journal-title":"Mach. Learn."},{"key":"10.1016\/j.patcog.2026.113481_bib0054","series-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24","first-page":"5644","article-title":"Denoising-aware contrastive learning for noisy time series","author":"Zhou","year":"2024"},{"key":"10.1016\/j.patcog.2026.113481_bib0055","series-title":"2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)","first-page":"244","article-title":"A survey of topic models in text classification","author":"Xia","year":"2019"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326004474?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326004474?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:17:05Z","timestamp":1776125825000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326004474"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":55,"alternative-id":["S0031320326004474"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113481","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Semi-supervised topic-guided contrastive learning with data augmentation for daily stress prediction using smartphone sensors","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113481","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113481"}}