poster

A Transformer Architecture for Stress Detection from ECG

Authors:

Behnam Behinaein,

Anubhav Bhatti,

Dirk Rodenburg,

Ali EtemadAuthors Info & Claims

ISWC '21: Proceedings of the 2021 ACM International Symposium on Wearable Computers

Pages 132 - 134

https://doi.org/10.1145/3460421.3480427

Published: 21 September 2021 Publication History

Abstract

Electrocardiogram (ECG) has been widely used for emotion recognition. This paper presents a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. We perform leave-one-subject-out experiments on two publicly available datasets, WESAD and SWELL-KW, to evaluate our method. Our experiments show that the proposed model achieves strong results, comparable or better than the state-of-the-art models for ECG-based stress detection on these two datasets. Moreover, our method is end-to-end, does not require handcrafted features, and can learn robust representations with only a few convolutional blocks and the transformer component.

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Cited By

Anwar AKhalifa YCoyle JSejdic E(2025)Transformers in biosignal analysis: A reviewInformation Fusion10.1016/j.inffus.2024.102697114(102697)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102697
Bahameish MStockman TRequena Carrión J(2024)Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability FeaturesSensors10.3390/s2410321024:10(3210)Online publication date: 18-May-2024
https://doi.org/10.3390/s24103210
Li JWashington P(2024)A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning StudyJMIR AI10.2196/521713(e52171)Online publication date: 10-May-2024
https://doi.org/10.2196/52171
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cover image ACM Conferences

ISWC '21: Proceedings of the 2021 ACM International Symposium on Wearable Computers

September 2021

220 pages

ISBN:9781450384629

DOI:10.1145/3460421

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 21 September 2021

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UbiComp '21

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UbiComp '21: The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 21 - 26, 2021

Virtual, USA

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Overall Acceptance Rate 38 of 196 submissions, 19%

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Cited By

Anwar AKhalifa YCoyle JSejdic E(2025)Transformers in biosignal analysis: A reviewInformation Fusion10.1016/j.inffus.2024.102697114(102697)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102697
Bahameish MStockman TRequena Carrión J(2024)Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability FeaturesSensors10.3390/s2410321024:10(3210)Online publication date: 18-May-2024
https://doi.org/10.3390/s24103210
Li JWashington P(2024)A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning StudyJMIR AI10.2196/521713(e52171)Online publication date: 10-May-2024
https://doi.org/10.2196/52171
Han YZhang PPark MLee U(2024)Systematic Evaluation of Personalized Deep Learning Models for Affect RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997248:4(1-35)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699724
Gupta DBhatti AParmar SDan CLiu YShen BLee S(2024)Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality ForecastingProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685724(382-386)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685724
Shome DGhahjaverestan NEtemad A(2024)NapTune: Efficient Model Tuning for Mood Classification using Previous Night's Sleep Measures along with Wearable Time-seriesProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685722(204-213)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685722
Pourpanah FEtemad A(2024)Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive SurveyACM Transactions on Computing for Healthcare10.1145/36708545:4(1-43)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3670854
Bussolan ABaraldo SGambardella LValente A(2024)Multimodal fusion stress detector for enhanced human-robot collaboration in industrial assembly tasks2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)10.1109/RO-MAN60168.2024.10731373(978-984)Online publication date: 26-Aug-2024
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Li CMao YHuang QXie WHe XWu J(2024)A Real-Time Emotion-Aware System Based on Wireless Body Area Network for IoMT ApplicationsIEEE Internet of Things Journal10.1109/JIOT.2024.345897611:24(41182-41193)Online publication date: 15-Dec-2024
https://doi.org/10.1109/JIOT.2024.3458976
Chen JHu YGarg LReddy Gadekallu TSrivastava GWang W(2024)Graph-Enhanced Low-Resource ECG Representation Learning for Emotion Recognition Based on Wearable Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2024.343029711:24(39056-39068)Online publication date: 15-Dec-2024
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