追踪生物信号对于监测健康状况、提前预防严重疾病的发展至关重要。如今，可穿戴设备能够便捷地记录各类生物信号，为在不干扰人们日常生活的前提下监测健康状态创造了条件。尽管可穿戴设备已得到广泛应用，且现有数字生物标志物也已投入使用，但缺乏带有标注医疗标签的整理后数据集，这阻碍了用于衡量常见健康状况的新型生物标志物的开发。事实上，与其他领域相比，医疗数据集通常规模较小，这是开发生物信号神经网络模型的一大障碍。为应对这一挑战，我们利用从大型纵向研究Apple Heart and Movement Study（AHMS，苹果心脏与运动研究）中经知情同意收集的未标注传感器数据，开展自监督学习，为Apple Watch记录的两种常见生物信号——光电容积脉搏波（PPG）和心电图（ECG）——训练基础模型。我们从AHMS中整理了PPG和ECG数据集，涵盖约14.1万名参与者、跨度约3年的数据。我们的自监督学习框架包含参与者级正样本对选择、随机增强模块以及通过动量训练优化的正则化对比损失，且能很好地泛化到PPG和ECG两种模态上。我们的研究表明，预训练后的基础模型能够轻松编码参与者的人口统计学特征与健康状况相关信息。据我们所知，这是首个使用消费级可穿戴设备收集的大规模PPG和ECG数据构建基础模型的研究——此前的研究通常使用在临床和实验环境中收集的规模更小的数据集。我们认为，PPG和ECG基础模型能够减少对标注数据的依赖，从而改进未来的可穿戴设备，并有潜力帮助用户改善健康状况。
英文摘要：
Tracking biosignals is crucial for monitoring wellness and preempting the development of severe medical conditions. Today, wearable devices can conveniently record various biosignals, creating the opportunity to monitor health status without disruption to one's daily routine. Despite widespread use of wearable devices and existing digital biomarkers, the absence of curated data with annotated medical labels hinders the development of new biomarkers to measure common health conditions. In fact, medical datasets are usually small in comparison to other domains, which is an obstacle for developing neural network models for biosignals. To address this challenge, we have employed self-supervised learning using the unlabeled sensor data collected under informed consent from the large longitudinal Apple Heart and Movement Study (AHMS) to train foundation models for two common biosignals: photoplethysmography (PPG) and electrocardiogram (ECG) recorded on Apple Watch. We curated PPG and ECG datasets from AHMS that include data from ~141K participants spanning ~3 years. Our self-supervised learning framework includes participant level positive pair selection, stochastic augmentation module and a regularized contrastive loss optimized with momentum training, and generalizes well to both PPG and ECG modalities. We show that the pre-trained foundation models readily encode information regarding participants' demographics and health conditions. To the best of our knowledge, this is the first study that builds foundation models using large-scale PPG and ECG data collected via wearable consumer devices $\unicode{x2013}$ prior works have commonly used smaller-size datasets collected in clinical and experimental settings. We believe PPG and ECG foundation models can enhance future wearable devices by reducing the reliance on labeled data and hold the potential to help the users improve their health.
Many-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems
Lund University（隆德大学）
将低功耗可穿戴物联网（IoT）系统集成到日常健康监测中是一项持续的挑战。可穿戴设备计算能力的最新进展使得通过利用多个生物信号和使用高性能算法（如深度神经网络（DNN））来针对复杂场景成为可能。然而，在算法的性能和具有有限资源的物联网平台的低功耗要求之间存在权衡。此外，身体更大和基于多生物信号的可穿戴设备给患者带来了明显的不适。因此，减少功耗和不适对于患者在日常生活中持续使用物联网设备是必要的。为了克服这些挑战，在癫痫发作检测的背景下，我们提出了一种多对一的信号知识蒸馏方法，目标是物联网可穿戴系统中的单生物信号处理。我们的出发点是获得一个高精度的多生物信号DNN，然后应用我们的方法为物联网系统开发一个单生物信号DNN解决方案，实现与原始多生物信号DNN相当的精度。为了评估我们的方法在现实生活场景中的实用性，我们在几个最先进的边缘计算平台上进行了全面的模拟实验分析，例如Kendryte K210和Raspberry Pi Zero。
NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models
Imperial College London（帝国理工学院伦敦分校）；Cogitat；National and Kapodistrian University of Athens（国家与资本主义大学雅典分校）；Archimedes Research Unit（阿基米德研究单位）；Aristotle University of Thessaloniki（亚里士多德大学塞萨洛尼基分校）；Northeastern University London（东北大学伦敦分校）
Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics
Department of Biomedical Engineering, Southern University of Science and Technology（南方科技大学生物医学工程系）；Omni-Intelligence（全知智能）；Shenzhen Loop Area Institute（深圳河套学院）
Biosignal Fingerprinting: A Cross-Modal PPG-ECG Foundation Model
Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Oxford Suzhou Centre for Advanced Research（牛津苏浙先进研究中心）；School of Public Health, Shanghai Jiao Tong University（上海交通大学公共卫生学院）
SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures
Graduate School of Data Science, Seoul National University, Seoul, South Korea（首尔国立大学数据科学研究生院，韩国首尔）；Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea（首尔国立大学医学院生物医学科学系，韩国首尔）；Obstructive Upper Airway Research (OUaR) Laboratory, Department of Pharmacology, Seoul National University College of Medicine, Seoul, Republic of Korea（首尔国立大学医学院药理学系阻塞性上气道研究（OUaR）实验室，韩国首尔）；Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea（首尔国立大学医院耳鼻喉头颈外科系，韩国首尔）
sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals
Five Seasons Medical（五 Seasons 医疗）；Tsinghua University（清华大学）；Beijing Key Laboratory for Sleep Breathing Disorder（北京睡眠呼吸障碍重点实验室）；Peking University（北京大学）；Fudan University（复旦大学）；Technical University of Munich（慕尼黑技术大学）
SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals
Georgia Institute of Technology（佐治亚理工学院）；Duke University（杜克大学）；Emory University（埃默里大学）；University of California at San Francisco（加州大学旧金山分校）；Nell Hodgson Woodruff School of Nursing, Emory University（埃默里大学内尔·霍奇森·伍德拉夫护理学院）
BioTrain: Sub-MB, Sub-50mW On-Device Fine-Tuning for Edge-AI on Biosignals
Integrated Systems Laboratory (IIS), ETH Zurich（集成系统实验室（IIS），苏黎世联邦理工学院）；Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna（电气、电子与信息工程系（DEI），博洛尼亚大学）；Department of Control and Computer Engineering (DAUIN), Politecnico di Torino（控制与计算机工程系（DAUIN），托里诺理工学院）
Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning
National Taiwan University（台湾大学）；National Center for High-Performance Computing（国家高速计算机中心）；National Institutes of Applied Research（应用研究院）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard University（哈佛大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；National Center for Theoretical Sciences（国家理论科学中心）
A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health
KIOS Research and Innovation Center of Excellence, University of Cyprus（塞浦路斯大学KIOS研究与创新卓越中心）；Department of Electrical and Computer Engineering, University of Cyprus（塞浦路斯大学电气与计算机工程系）
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）
CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment
Department of Computer Science, College of AI, Cyber and Computing, The University of Texas at San Antonio（计算机科学系，人工智能、网络与计算学院，德克萨斯大学圣安东尼奥分校）；Department of Educational Psychology, College of Education and Human Development, The University of Texas at San Antonio（教育心理学系，教育与人类发展学院，德克萨斯大学圣安东尼奥分校）
Detecting Daily Living Gait Amid Huntington's Disease Chorea using a Foundation Deep Learning Model
Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）；Teachers College, Columbia University（哥伦比亚大学教师学院）；Wayne State University（韦恩州立大学）；Stony Brook University（石溪大学）；Rush University Medical Center（拉什大学医学中心）
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）
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）
FM-FoG: A Real-Time Foundation Model-based Wearable System for Freezing-of-Gait Mitigation
Department of Computer Science, William \& Mary Virginia USA；Department of Neurology, Virginia Commonwealth University Virginia USA；School of Nursing, Virginia Commonwealth University Virginia USA；Department of Computer Science, William \& Mary；Department of Neurology, Virginia Commonwealth University；School of Nursing, Virginia Commonwealth University
