本文提出AI on the Pulse系统，基于UniTS模型融合可穿戴与环境智能进行实时异常检测，优于12种方法，F1提升约22%，并已在居家场景部署，实现无需临床设备的持续监测与可解释告警。
摘要：
我们推出了AI on the Pulse，一个面向实际应用的异常检测系统，该系统通过融合可穿戴传感器、环境智能和先进AI模型，持续监测患者。该系统由UniTS（一种最先进的通用时间序列模型）驱动，能够自主学习每位患者独特的生理和行为模式，检测出预示潜在健康风险的细微偏差。与需要在实际场景中进行不切实际的持续标注的分类方法不同，我们的方法采用异常检测，为反应性居家护理干预提供实时、个性化的警报。我们的方法优于12种最先进的异常检测方法，在高保真医疗设备（心电图）和消费级可穿戴设备上均展现出鲁棒性，F1分数提升约22%。然而，AI on the Pulse的真正影响力体现在@HOME，该系统已成功部署用于持续、真实世界的患者监测。通过使用智能手表等非侵入性、轻量级设备，我们的系统证明了无需临床级设备也能实现高质量的健康监测。除了检测之外，我们通过集成大语言模型增强了可解释性，将异常分数转化为对医疗保健专业人员具有临床意义的见解。
英文摘要：
We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced AI models. Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns, detecting subtle deviations that signal potential health risks. Unlike classification methods that require impractical, continuous labeling in real-world scenarios, our approach uses anomaly detection to provide real-time, personalized alerts for reactive home-care interventions. Our approach outperforms 12 SoTA anomaly detection methods, demonstrating robustness across both high-fidelity medical devices (ECG) and consumer wearables, with a ~ 22% improvement in F1 score. However, the true impact of AI on the Pulse lies in @HOME, where it has been successfully deployed for continuous, real-world patient monitoring. By operating with non-invasive, lightweight devices like smartwatches, our system proves that high-quality health monitoring is possible without clinical-grade equipment. Beyond detection, we enhance interpretability by integrating LLMs, translating anomaly scores into clinically meaningful insights for healthcare professionals.
A Foundation Model for Wearable Movement Data in Mental Health Research
Dartmouth College（达特茅斯学院）；National Institute of Diabetes and Digestive and Kidney Diseases（国家糖尿病、消化系统疾病和肾病研究所）；National Institutes of Health（美国国立卫生研究院）；Department of Computer Science at Dartmouth College（达特茅斯学院计算机科学系）
REMONI: An Autonomous System Integrating Wearables and Multimodal Large Language Models for Enhanced Remote Health Monitoring
2 Department of Electrical；Computer Engineering University of Waterloo, Waterloo, ON, Canada N2L 3G1 Email；3 College of Computer；Information Sciences Prince Sultan University Email
College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；Department of Computer Science, University of Memphis（孟菲斯大学计算机科学系）
The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-line setting. In this paper, we propose a novel approach to learning shallow detection cascades developed explicitly for use in a real-time wearable-phone or wearable-phone-cloud systems. We apply our approach to the problem of cigarette smoking detection from a combination of wri…
Wearable Foundation Models Should Go Beyond Static Encoders
Dartmouth College, USA（达特茅斯学院）；University of Cambridge, UK（剑桥大学）；The University of Melbourne, Australia（墨尔本大学）；Google Research, UK（谷歌研究）；Tsinghua University, China（清华大学）
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors
Aston University（阿斯顿大学）；University of Toronto（多伦多大学）；University Health Network（大学健康网络）；Florida Institute of Technology（佛罗里达理工学院）；Hai Duong Central College of Pharmacy（海洞中央药学院）；Stanford University（斯坦福大学）；Industrial University of Ho Chi Minh City（胡志明市工业大学）；The University of Alabama at Birmingham（伯明翰大学）；Pukyong National University（浦项国立大学）
Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common scenario in many applications, but may not always be feasible in real-world scenarios. For example, although combining bio-signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-li…
Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院）
A person's passage of time perception (POTP) is strongly linked to their mental state and stress response, and can therefore provide an easily quantifiable means of continuous mental health monitoring. In this work, we develop a custom experiment and Machine Learning (ML) models for predicting POTP from biomarkers acquired from wearable biosensors. We first confirm that individuals experience time passing slower than usual during fear or sadness (p = 0.046) and faster than usual during cognitive tasks (p = 2 x 10^-5). Then, we group together the experimental segments associated with fast, slow…
Explainable AI Using Inherently Interpretable Components for Wearable-based Health Monitoring
Signal and System Theory Group, Paderborn University（信号与系统理论组，帕德博恩大学）；Department of Medicine I, Ludwig Maximilian University（医学部I，路德维希-马克西米利安大学）；Konrad Zuse School of Excellence in Reliable AI, Munich（可靠性人工智能卓越学校，慕尼黑）；Institute of Sports Medicine, Paderborn University（运动医学研究所，帕德博恩大学）；Division of Sports Neurology and Neurosciences, Mass General Brigham（运动神经学与神经科学部，马萨诸塞总医院布里奇顿分院）；Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School（癫痫与临床神经生理学部，波士顿儿童医院，哈佛医学院）
With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthca…
With the advancement of technologies, market wearables are becoming increasingly popular with a range of services, including providing access to bank accounts, accessing cars, monitoring patients remotely, among several others. However, often these wearables collect various sensitive personal information of a user with no to limited authentication, e.g., knowledge-based external authentication techniques, such as PINs. While most of these external authentication techniques suffer from multiple limitations, including recall burden, human errors, or biases, researchers have started using various…
The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These smart health monitoring systems could be realized in a smart home context to render long-term care to the elderly population. In this paper, we present the design of a wearable health monitoring system suitable for older adults in a smart home context. The proposed system offers solutions to monitor the stress, blood pressure, and location of an individual within a smart home environment. The stress detection model proposed in this work uses Electrodermal Activity (EDA), Photoplethysmogr…
Wearable devices that integrate multiple sensors, processors, and communication technologies have the potential to transform mobile health for remote monitoring of health parameters. However, the small form factor of the wearable devices limits the battery size and operating lifetime. As a result, the devices require frequent recharging, which has limited their widespread adoption. Energy harvesting has emerged as an effective method towards sustainable operation of wearable devices. Unfortunately, energy harvesting alone is not sufficient to fulfill the energy requirements of wearable devices…
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…
Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?
Sano Centre for Computational Medicine（萨诺计算医学中心）；Poznan University of Technology（波兹南理工大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
University of Minnesota（明尼苏达大学）；Nanyang Technological University（南洋理工大学）；QCRI（卡塔尔计算研究所）
Physical activity and sleep play a major role in the prevention and management of many chronic conditions. It is not a trivial task to understand their impact on chronic conditions. Currently, data from electronic health records (EHRs), sleep lab studies, and activity/sleep logs are used. The rapid increase in the popularity of wearable health devices provides a significant new data source, making it possible to track the user's lifestyle real-time through web interfaces, both to consumer as well as their healthcare provider, potentially. However, at present there is a gap between lifestyle da…
Vast quantities of person-generated health data (wearables) are collected but the process of annotating to feed to machine learning models is impractical. This paper discusses ways in which self-supervised approaches that use contrastive losses, such as SimCLR and BYOL, previously applied to the vision domain, can be applied to high-dimensional health signals for downstream classification tasks of various diseases spanning sleep, heart, and metabolic conditions. To this end, we adapt the data augmentation step and the overall architecture to suit the temporal nature of the data (wearable trace…
Thayer School of Engineering（塞耶工程学院）；Dartmouth College（达特茅斯学院）
In this paper, we present a system that employs a wearable acoustic sensor and a deep convolutional neural network for detecting coughs. We evaluate the performance of our system on 14 healthy volunteers and compare it to that of other cough detection systems that have been reported in the literature. Experimental results show that our system achieves a classification sensitivity of 95.1% and a specificity of 99.5%.
Modern smart phones are becoming helpful in the areas of Internet-Of-Things (IoT) and ambient health intelligence. By learning data from several mobile sensors, we detect nearness of the human body to a mobile device in a three-dimensional space with no physical contact with the device for non-invasive health diagnostics. We show that the human body generates wave patterns that interact with other naturally occurring ambient signals that could be measured by mobile sensors, such as, temperature, humidity, magnetic field, acceleration, gravity, and light. This interaction consequentially alters…
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）
CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms
Yale School of Medicine（耶鲁医学院）；Yale New Haven Hospital（耶鲁-纽黑文医院）；Yale School of Public Health（耶鲁公共卫生学院）
While physical activity has been described as a primary prevention against chronic diseases, strenuous physical exertion under adverse ambient conditions has also been reported as a major contributor to exacerbation of chronic respiratory conditions. Maintaining a balance by monitoring the type and the level of physical activities of affected individuals, could help in reducing the cost and burden of managing respiratory ailments. This paper explores the potentiality of motion sensors in Smartphones to estimate physical activity thresholds that could trigger symptoms of exercise induced respir…
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…
Assessing the health of both the fetus and mother is vital in preventing and identifying possible complications in pregnancy. This paper focuses on a device that can be used effectively by the mother herself with minimal supervision and provide a reasonable estimation of fetal and maternal health while being safe, comfortable, and easy to use. The device proposed uses a belt with a single accelerometer over the mother's uterus to record the required information. The device is expected to monitor both the mother and the fetus constantly over a long period and provide medical professionals with …
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, …
University of Cambridge（剑桥大学）；MRC Epidemiology Unit（英国医学研究委员会流行病学组）
Deep learning models have shown great promise in various healthcare applications. However, most models are developed and validated on small-scale datasets, as collecting high-quality (gold-standard) labels for health applications is often costly and time-consuming. As a result, these models may suffer from overfitting and not generalize well to unseen data. At the same time, an extensive amount of data with imprecise labels (silver-standard) is starting to be generally available, as collected from inexpensive wearables like accelerometers and electrocardiography sensors. These currently underu…
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…
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems
Institute of Computer Engineering, Heidelberg University（海德堡大学计算机工程研究所）；Faculty of Computer Engineering, K. N. Toosi University of Technology（霍加·纳赛尔·图西大学计算机工程学院）；Department of Electrical and Information Technology, Lund University（隆德大学电气与信息技术系）
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；University of Maryland, Baltimore（马里兰大学巴尔的摩分校）
Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k.a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal process…
MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Tehran（德黑兰大学）；Institute for Research in Fundamental Sciences (IPM)（基础科学研究所）；Lund University（隆德大学）
