Contrastive Pretraining for Stress Detection with Multimodal Wearable Sensor Data and Surveys.

Zeyu Yang, Han Yu, Akane Sano

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zeyu YangDepartment of Electrical and Computer Engineering, Rice University.

Han YuDepartment of Electrical and Computer Engineering, Rice University.

Akane SanoDepartment of Electrical and Computer Engineering, Rice University.

Funding

SCH: Multidimensional Data Science Approach: Measuring and Characterizing Craving and Affective Profiles in Substance Use DisordersR01DA059925 · NIDA · RICE UNIVERSITY · PI Nidal Moukaddam, Akane Sano · 2023 to 2026

$1.2M

Using moment-to-moment sensors, mobile technology, and stimulated cytokine responses to identify physical and mental health risk among Alzheimer's spousal caregiversK25AG070306 · NIA · RICE UNIVERSITY · PI SANO, AKANE · 2021 to 2023

$432k

NIA NIH HHS K25 AG070306NIDA NIH HHS R01 DA059925

6 · The paper itself

Abstract

Stress adversely affects mental and physical health and underscores the importance of early detection. Some studies have utilized physiological signals from wearable sensors and other information to monitor stress levels in daily life. Recent studies use self-supervised methods due to the high cost of collecting stress labels. However, self-supervised learning using both time series and tabular features such as demographics, traits, and contextual information has been understudied. Therefore, there is a need to further investigate how a model can be effectively trained with different granularity of multimodal data and a limited number of labels. In this study, we introduce a self-supervised multimodal learning approach for stress detection that combines time series and tabular features. Our proposed method presents a promising solution for effectively monitoring stress using multimodal data.

Identifiers

PMID41816559

PMCPMC12975034

What OpenQuestion holds

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LicenceTDM

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.