Kyungtaek Oh , Hyunseo Park , Gyeong Ho Lee , Jun Kyun Choi
{"title":"基于可穿戴传感器的医疗保健应用的基于监督对比学习的应力检测","authors":"Kyungtaek Oh , Hyunseo Park , Gyeong Ho Lee , Jun Kyun Choi","doi":"10.1016/j.future.2025.108058","DOIUrl":null,"url":null,"abstract":"<div><div>Advancements in Internet of Things (IoT) technology have enabled continuous physiological monitoring through wearable devices, significantly improving personalized healthcare services. Automated stress detection based on physiological signals is a critical function in digital health systems, enabling timely interventions and reducing severe health risks. Despite recent progress in deep learning, effectively capturing both general human stress patterns and individual-specific variations remains a core challenge. To address this, we propose StressCon, a novel deep learning framework that fully integrates contrastive learning with metadata fusion to learn robust stress patterns while adapting to individual characteristics. Furthermore, our method jointly optimizes two contrastive loss functions to learn subject-invariant features, and then leverages user metadata for personalized stress detection. We comprehensively evaluate the proposed method on two publicly available datasets using Leave-One-Subject-Out (LOSO) validation. Results show that StressCon enhances classification accuracy by up to 3.49% and F1-score by up to 3.48%, while maintaining consistent state-of-the-art performance across diverse populations. These findings confirm the superior generalization capabilities and practical applicability of the proposed approach, demonstrating an effective balance between population-level robustness and individual personalization for IoT-based stress monitoring systems.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"175 ","pages":"Article 108058"},"PeriodicalIF":6.2000,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Supervised contrastive learning-based stress detection for wearable sensor-based healthcare applications\",\"authors\":\"Kyungtaek Oh , Hyunseo Park , Gyeong Ho Lee , Jun Kyun Choi\",\"doi\":\"10.1016/j.future.2025.108058\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Advancements in Internet of Things (IoT) technology have enabled continuous physiological monitoring through wearable devices, significantly improving personalized healthcare services. Automated stress detection based on physiological signals is a critical function in digital health systems, enabling timely interventions and reducing severe health risks. Despite recent progress in deep learning, effectively capturing both general human stress patterns and individual-specific variations remains a core challenge. To address this, we propose StressCon, a novel deep learning framework that fully integrates contrastive learning with metadata fusion to learn robust stress patterns while adapting to individual characteristics. Furthermore, our method jointly optimizes two contrastive loss functions to learn subject-invariant features, and then leverages user metadata for personalized stress detection. We comprehensively evaluate the proposed method on two publicly available datasets using Leave-One-Subject-Out (LOSO) validation. Results show that StressCon enhances classification accuracy by up to 3.49% and F1-score by up to 3.48%, while maintaining consistent state-of-the-art performance across diverse populations. These findings confirm the superior generalization capabilities and practical applicability of the proposed approach, demonstrating an effective balance between population-level robustness and individual personalization for IoT-based stress monitoring systems.</div></div>\",\"PeriodicalId\":55132,\"journal\":{\"name\":\"Future Generation Computer Systems-The International Journal of Escience\",\"volume\":\"175 \",\"pages\":\"Article 108058\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2025-08-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Generation Computer Systems-The International Journal of Escience\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167739X2500353X\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X2500353X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
Supervised contrastive learning-based stress detection for wearable sensor-based healthcare applications
Advancements in Internet of Things (IoT) technology have enabled continuous physiological monitoring through wearable devices, significantly improving personalized healthcare services. Automated stress detection based on physiological signals is a critical function in digital health systems, enabling timely interventions and reducing severe health risks. Despite recent progress in deep learning, effectively capturing both general human stress patterns and individual-specific variations remains a core challenge. To address this, we propose StressCon, a novel deep learning framework that fully integrates contrastive learning with metadata fusion to learn robust stress patterns while adapting to individual characteristics. Furthermore, our method jointly optimizes two contrastive loss functions to learn subject-invariant features, and then leverages user metadata for personalized stress detection. We comprehensively evaluate the proposed method on two publicly available datasets using Leave-One-Subject-Out (LOSO) validation. Results show that StressCon enhances classification accuracy by up to 3.49% and F1-score by up to 3.48%, while maintaining consistent state-of-the-art performance across diverse populations. These findings confirm the superior generalization capabilities and practical applicability of the proposed approach, demonstrating an effective balance between population-level robustness and individual personalization for IoT-based stress monitoring systems.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.