Health metrics from wrist-worn devices demand an automatic dominant hand prediction to keep an accurate operation. The prediction would improve reliability, enhance the consumer experience, and encourage further development of healthcare applications. This paper aims to evaluate the use of physiological and spatiotemporal context information from a two-hand experiment to predict the wrist placement of a commercial smartwatch. The main contribution is a methodology to obtain an effective model and features from low sample rate physiological sensors and a self-reported context survey. Results show an effective dominant hand prediction using data from a single subject under real-life conditions.
Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
National University of Singapore（新加坡国立大学）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；MIT（麻省理工学院）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）
Predicting and Interpolating Spatiotemporal Environmental Data: A Case Study of Groundwater Storage in Bangladesh
Department of Computer Science, Brunel University of London, UK（布鲁内尔大学伦敦计算机科学系）；Department of Geography, UCL, London, UK（伦敦大学学院地理系）；Department of Risk and Disaster Reduction, UCL, London, UK（伦敦大学学院风险与灾害减少系）
Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage
Centre for Computational Science, University College London（伦敦大学学院计算科学中心）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学院）；Centre for Advanced Research Computing, University College London（伦敦大学学院高级计算研究中心）
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）
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）
Dynamic Graph Neural Networks for Physiological Based Pharmacokinetic Modeling: A Novel Data Driven Approach to Drug Concentration Prediction
Independent Researcher（独立研究者）；University of Michigan -- Ann Arbor（密歇根大学安娜堡分校）；Texas A\&M University（德克萨斯农工大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of California -- Irvine（加州大学伊尔弗分校）
PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Engineering Science, University of Chinese Academy of Sciences（中国科学院大学工程科学学院）；Huawei Technologies（华为技术有限公司）
Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction
Department of Mathematics and Systems Engineering（数学与系统工程系）；Florida Institute of Technology（佛罗里达理工学院）；Department of Mathematics（数学系）；Embry-Riddle Aeronautical University（埃姆布里-瑞德尔航空大学）；Department of Mechanical and Civil Engineering（机械与土木工程系）
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
IDSIA USI-SUPSI（IDSIA USI-SUPSI（瑞士意大利语大学与瑞士应用科学大学联合机构））；Università della Svizzera italiana（瑞士意大利语大学）；Politecnico di Milano（米兰理工大学）；UiT the Arctic University of Norway（挪威北极大学）；NORCE, Norwegian Research Centre AS（NORCE挪威研究中心）
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction
Renmin University of China（中国人民大学）；Northeastern University（东北大学）；Huawei Technologies（华为技术有限公司）；HKUST（香港科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
AFD-STA: Adaptive Filtering Denoising with Spatiotemporal Attention for Chaotic System Prediction
University of Minnesota Twin Cities（明尼苏达大学双城分校）；School of Statistics and Mathematics, Shandong University of Finance and Economics（山东财经大学统计与数学学院）；Jinan Fengdi Intelligent Electronics Co., Ltd.（济南风迪智能电子有限公司）；Shandong Zhike Intelligence Computing Co., Ltd.（山东智科智能计算有限公司）；School of Mechanical,Electrical and Information Engineering, Shandong University（山东大学机械电子信息工程学院）
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Alabama, Tuscaloosa（阿拉巴马大学塔斯卡洛osa分校）；University of Florida（佛罗里达大学）
Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学交通工程系和道路与交通工程重点实验室）
M-TabNet: A Multi-Encoder Transformer Model for Predicting Neonatal Birth Weight from Multimodal Data
Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili (URV)（计算机工程与数学系，罗维拉-维加利大学）；Unit of Preventive Medicine & Biostatistics, Faculty of Medicine and Health Sciences, URV, CIBERObn ISCIII（预防医学与生物统计学单位，医学与健康科学学院，URV，CIBERObn ISCIII）
Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder
University College London（伦敦大学学院）；Imperial College London（帝国理工学院）；University of Liverpool（利物浦大学）；ENPC, EDF R&D, Institut Polytechnique de Paris（巴黎高科国立路桥学校，法国电力集团研发部，巴黎综合理工学院）
STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data
State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau（物联网智能城市国家重点实验室和计算机与信息科学系，澳门大学）；Beijing Normal University-Hong Kong Baptist University United International College（北京师范大学-香港 Baptist大学联合国际学院）
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking
