Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction difficult in practice. To counter these issues, we have developed a multimodal personalized stress prediction system using wearable biosignal data. We employ self-supervised learning (SSL) to pre-train the models on each subject's data, allowing the models to learn the baseline dynamics of the participant's biosignals prior to fine-tuning the stress prediction task. We test our model on the Wearable Stress and Affect Detection (WESAD) dataset, demonstrating that our SSL models outperform non-SSL models while utilizing less than 5% of the annotations. These results suggest that our approach can personalize stress prediction to each user with minimal annotations. This paradigm has the potential to enable personalized prediction of a variety of recurring health events using complex multimodal data streams.
Revolutionizing Personalized Cancer Vaccines with NEO: Novel Epitope Optimization Using an Aggregated Feed Forward and Recurrent Neural Network with LSTM Architecture
AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals
Engineering and Technology institute Groningen (ENTEG), University of Groningen（格罗宁根大学工程与技术学院（ENTEG））；Department of Human Movement Sciences, University Medical Center Groningen（格罗宁根大学医学中心人类运动科学系）
Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
Engineering and Technology Institute Groningen, University of Groningen（Groningen大学工程与技术学院）；Department of Human Movement Sciences, University of Groningen, University Medical Center Groningen（Groningen大学人类运动科学系）
Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks
Univ. Bordeaux, CNRS, Bordeaux INP, I2M, UMR 5295（波尔多大学、国家科学研究中心、波尔多工业学院、I2M、UMR 5295）；Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800（波尔多大学、国家科学研究中心、波尔多工业学院、LaBRI、UMR 5800）；Institute of Mechanics and Fluid Dynamics, TU Bergakademie Freiberg（力学与流体动力学研究所、弗赖堡技术大学）；Univ. Grenoble Alpes, CNRS, UMR 5525（格勒诺布尔阿尔卑斯大学、国家科学研究中心、UMR 5525）；Arts et Metiers Institute of Technology, CNRS, Bordeaux INP, I2M, UMR 5295（艺术与工艺技术学院、国家科学研究中心、波尔多工业学院、I2M、UMR 5295）
Developing hybrid mechanistic and data-driven personalized prediction models for platelet dynamics
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität Leipzig（德累斯顿/莱比锡可扩展数据分析与人工智能中心（ScaDS.AI），莱比锡大学）；Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universität Leipzig（莱比锡大学医学信息学、统计学与流行病学研究所（IMISE））
Employing Multimodal Machine Learning for Stress Detection
Symbiosis Institute of Technology, Symbiosis International (Deemed University)；Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University)
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions
Department of Industrial Engineering and Management Systems University of Central Florida（工业工程与管理系统系 佛罗里达中央大学）；Institute for Simulation and Training University of Central Florida（模拟与培训研究所 佛罗里达中央大学）；Department of Internal Medicine University of Central Florida（内科医学系 佛罗里达中央大学）；Department of Statistics and Data Science University of Central Florida（统计学与数据科学系 佛罗里达中央大学）
Neural Mixed Effects for Nonlinear Personalized Predictions
Carnegie Mellon University（卡内基梅隆大学）；University of Oregon（俄勒冈大学）；Oregon Research Institute（俄勒冈研究所）；Columbia University（哥伦比亚大学）；University of Pittsburgh（匹兹堡大学）
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
Claremont Graduate University（克莱蒙特研究生大学）；Pomona College（波莫纳学院）；San Diego State University（圣地亚哥州立大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Broadridge Financial Solutions（百瑞吉金融解决方案公司）；Institute of Mathematical Sciences（数学科学研究所）；Mathematics Department（数学系）；Computational Science Research Center（计算科学研究中心）；Earth and Environmental Sciences Division（地球与环境科学部）
The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress
University of Augsburg（奥格斯堡大学）；Hume AI（休谟人工智能）；University of Passau（帕绍大学）；Recoro；University of Ulm（乌尔姆大学）；Nanyang Technological University（南洋理工大学）；Imperial College London（伦敦帝国理工学院）
