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）
Xueyi Wang, Claudine J. C. Lamoth, Elisabeth Wilhelm
Continuous stress forecasting could potentially contribute to lifestyle interventions. This paper presents a novel, explainable, and individualized approach for stress prediction using physiological data from consumer-grade smartwatches. We develop a time series forecasting model that leverages multivariate features, including heart rate variability, activity patterns, and sleep metrics, to predict stress levels across 16 temporal horizons (History window: 3, 5, 7, 9 days; forecasting window: 1, 3, 5, 7 days). Our evaluation involves 16 participants monitored for 10-15 weeks. We evaluate our approach across 16 participants, comparing against state-of-the-art time series models (Informer, TimesNet, PatchTST) and traditional baselines (CNN, LSTM, CNN-LSTM) across multiple temporal horizons. Our model achieved performance with an MSE of 0.053, MAE of 0.190, and RMSE of 0.226 in optimal settings (5-day input, 1-day prediction). A comparison with the baseline models shows that our model outperforms TimesNet, PatchTST, CNN-LSTM, LSTM, and CNN under all conditions, representing improvements of 36.9%, 25.5%, and 21.5% over the best baseline. According to the explanability analysis, sleep metrics are the most dominant and consistent stress predictors (importance: 1.1, consistency: 0.9-1.0), while activity features exhibit high inter-participant variability (0.1-0.2). Most notably, the model captures individual-specific patterns where identical features can have opposing effects across users, validating its personalization capabilities. These findings establish that consumer wearables, combined with adaptive and interpretable deep learning, can deliver relevant stress assessment adapted to individual physiological responses, providing a foundation for scalable, continuous, explainable mental health monitoring in real-world settings.
Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting
Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Sweden（诊断与介入部门，辐射物理，生物医学工程，乌梅大学，瑞典）；Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy（海军、电气、电子与电信工程部门，热那亚大学，意大利）
Ricoh Software Research Center (Beijing) Co., Ltd.（理光软件研究所（北京）有限公司）；Japan Advanced Institute of Science and Technology (JAIST)（日本北陆先端科学技术大学院大学（JAIST））；Yale University School of Medicine（耶鲁大学医学院）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）
Machine learning has been widely used in healthcare applications to approximate complex models, for clinical diagnosis, prognosis, and treatment. As deep learning has the outstanding ability to extract information from time series, its true capabilities on sparse, irregularly sampled, multivariate, and imbalanced physiological data are not yet fully explored. In this paper, we systematically examine the performance of machine learning models for the clinical prediction task based on the EHR, especially physiological time series. We choose Physionet 2019 challenge public dataset to predict Seps…
Eindhoven University of Technology（埃因霍温理工大学）；Philips Research（飞利浦研究院）
Stress can be seen as a physiological response to everyday emotional, mental and physical challenges. A long-term exposure to stressful situations can have negative health consequences, such as increased risk of cardiovascular diseases and immune system disorder. Therefore, a timely stress detection can lead to systems for better management and prevention in future circumstances. In this paper, we suggest a multi-task learning based neural network approach (with hard parameter sharing of mutual representation and task-specific layers) for personalized stress recognition using skin conductance …
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Massachusetts Institute of Technology（麻省理工学院）
Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset …
Personalized Convolutional Dictionary Learning of Physiological Time Series
Centre Borelli（博雷利中心）；Thales AVS（泰雷兹AVS公司）；ENS Paris-Saclay（巴黎萨克雷高等师范学校）；University of Pennsylvania（宾夕法尼亚大学）；CMAP（应用数学与计算机科学中心）；CNRS（法国国家科学研究中心）；Ecole polytechnique（巴黎综合理工学院）
University of Washington（华盛顿大学）；OctoML；Microsoft Research AI（微软研究院AI）
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle to generalize well to unseen subjects or contexts and can often contain problematic biases. Video-based physiological measurement is not an exception. Therefore, learning personalized or customized models from a small number of unlabeled samples is very attractive as it would allow fast calibrations to improve generalization and help correct biases. In this paper, we present a novel meta-learning approach called MetaP…
From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）
A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series
Sapienza University of Rome Rome Italy；ISTC-CNR \& Sapienza University of Rome Rome Italy；Technical University of Munich Munich Germany；Sapienza University of Rome；ISTC-CNR \& Sapienza University of Rome；Technical University of Munich
Pre-Ictal Seizure Prediction Using Personalized Deep Learning
University of Maryland School of Medicine（马里兰大学医学院）；Thomas Jefferson High School for Science and Technology（托马斯杰斐逊科技高中）；The R Adam Cowley Shock Trauma Center, University of Maryland School of Medicine（马里兰大学医学院R·亚当·考利休克创伤中心）
As the result of the growing importance of the Human Computer Interface system, understanding human's emotion states has become a consequential ability for the computer. This paper aims to improve the performance of emotion recognition by conducting the complexity analysis of physiological signals. Based on AMIGOS dataset, we extracted several entropy-domain features such as Refined Composite Multi-Scale Entropy (RCMSE), Refined Composite Multi-Scale Permutation Entropy (RCMPE) from ECG and GSR signals, and Multivariate Multi-Scale Entropy (MMSE), Multivariate Multi-Scale Permutation Entropy (…
Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time. However, resource constraints on most of these wearable devices prevent the ability to perform online learning on them. To address this issue, it is required to rethink the machine learning mo…
Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals
Halıcıoğlu Data Science Institute, University of California San Diego（哈利奇奥格鲁数据科学研究所，加州大学圣地亚哥分校）；Schools of Engineering, Arizona State University（工程学院，亚利桑那州立大学）
PMB-NN: Physiology-Centred Hybrid AI for Personalized Hemodynamic Monitoring from Photoplethysmography
Department of Biomedical Signals and Systems, University of Twente（乌得勒支理工大学生物医学信号与系统系）；Department of Cardiovascular and Respiratory Physiology, University of Twente（乌得勒支理工大学心血管与呼吸生理学系）；Department of Intensive Care, University Medical Center Utrecht（乌得勒支大学医学中心重症医学科）
University of Cambridge（剑桥大学）；Alan Turing Institute（艾伦·图灵研究所）
To date, research on sensor-equipped mobile devices has primarily focused on the purely supervised task of human activity recognition (walking, running, etc), demonstrating limited success in inferring high-level health outcomes from low-level signals, such as acceleration. Here, we present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels. With a deep neural network, we set HR responses as the supervisory signal for the activity data, leveraging their underlying physiological relationship. We evaluate our model in the lar…
The presented first-of-its-kind study effectively identifies and visualizes the second-by-second pattern differences in the physiological arousal of preschool-age children who do stutter (CWS) and who do not stutter (CWNS) while speaking perceptually fluently in two challenging conditions i.e speaking in stressful situations and narration. The first condition may affect children's speech due to high arousal; the latter introduces linguistic, cognitive, and communicative demands on speakers. We collected physiological parameters data from 70 children in the two target conditions. First, we adop…
Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis
Laboratory of Hemodynamics and Cardiovascular Technology, EPFL（EPFL血液动力学与心血管技术实验室）；Stanford DBDS and HAI, Stanford University（斯坦福大学DBDS与HAI）；Medical School, National and Kapodistrian University of Athens（雅典大学医学院）
University of Toronto（多伦多大学）；Rubikloud Technologies Inc.（Rubikloud科技公司）
Access to a large variety of data across a massive population has made it possible to predict customer purchase patterns and responses to marketing campaigns. In particular, accurate demand forecasts for popular products with frequent repeat purchases are essential since these products are one of the main drivers of profits. However, buyer purchase patterns are extremely diverse and sparse on a per-product level due to population heterogeneity as well as dependence in purchase patterns across product categories. Traditional methods in survival analysis have proven effective in dealing with cen…
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University（江苏大学附属昆山医院骨科部）；Tianjin University Chest Hospital（天津大学胸科医院）；Haihe Hospital, Tianjin University（天津大学海河医院）；Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（广西师范大学教育区块链与智能技术重点实验室）
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging. In this paper, we present a general platform for personalized predictive modeling of behavioural states like students' level of stress. Through the use of Auto-encoders and Multitask learning we extend the prediction of stress to both sequences of passive sensor data and high-level covariates. Our model outperforms the state-of-the-art i…
Multimodal wearable physiological data in daily life have been used to estimate self-reported stress labels. However, missing data modalities in data collection makes it challenging to leverage all the collected samples. Besides, heterogeneous sensor data and labels among individuals add challenges in building robust stress detection models. In this paper, we proposed a modality fusion network (MFN) to train models and infer self-reported binary stress labels under both complete and incomplete modality conditions. In addition, we applied personalized attention (PA) strategy to leverage persona…
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the at…
University of Cambridge（剑桥大学）；Alan Turing Institute（阿兰·图灵研究所）
Wearable devices such as smartwatches are becoming increasingly popular tools for objectively monitoring physical activity in free-living conditions. To date, research has primarily focused on the purely supervised task of human activity recognition, demonstrating limited success in inferring high-level health outcomes from low-level signals. Here, we present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels. With a deep neural network, we set HR responses as the supervisory signal for the activity data, leveraging their u…
Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although several studies have evaluated stress levels in controlled settings, objective stress assessment in everyday settings is still largely under-explored due to challenges arising from confounding contextual factors and limited adherence for self-reports. In this paper, we explore the objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) captured via low-cos…
Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics
University of California San Diego（加州大学圣地亚哥分校）；Dartmouth College（达特茅斯学院）；Amazon Web Services（亚马逊网络服务）；Sanderling Renal Services（Sanderling 肾脏服务）；Renal Research Institute（肾脏研究研究所）；Icahn School of Medicine at Mount Sinai（辛克尔医学院（Mount Sinai））
College of Information and Computer Sciences（信息与计算机科学学院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network. This work is motivated by the analysis of physiological time serie…
Increased use of sensor signals from wearable devices as rich sources of physiological data has sparked growing interest in developing health monitoring systems to identify changes in an individual's health profile. Indeed, machine learning models for sensor signals have enabled a diverse range of healthcare related applications including early detection of abnormalities, fertility tracking, and adverse drug effect prediction. However, these models can fail to account for the dependent high-dimensional nature of the underlying sensor signals. In this paper, we introduce Latent Temporal Flows, …
We present PSEUDo, an adaptive feature learning technique for exploring visual patterns in multi-track sequential data. Our approach is designed with the primary focus to overcome the uneconomic retraining requirements and inflexible representation learning in current deep learning-based systems. Multi-track time series data are generated on an unprecedented scale due to increased sensors and data storage. These datasets hold valuable patterns, like in neuromarketing, where researchers try to link patterns in multivariate sequential data from physiological sensors to the purchase behavior of p…
Motivation: HIV is difficult to treat because its virus mutates at a high rate and mutated viruses easily develop resistance to existing drugs. If the relationships between mutations and drug resistances can be determined from historical data, patients can be provided personalized treatment according to their own mutation information. The HIV Drug Resistance Database was built to investigate the relationships. Our goal is to build a model using data in this database, which simultaneously predicts the resistance of multiple drugs using mutation information from sequences of viruses for any new …
