In this work, we apply information theory inspired methods to quantify changes in daily activity patterns. We use in-home movement monitoring data and show how they can help indicate the occurrence of healthcare-related events. Three different types of entropy measures namely Shannon's entropy, entropy rates for Markov chains, and entropy production rate have been utilised. The measures are evaluated on a large-scale in-home monitoring dataset that has been collected within our dementia care clinical study. The study uses Internet of Things (IoT) enabled solutions for continuous monitoring of in-home activity, sleep, and physiology to develop care and early intervention solutions to support people living with dementia (PLWD) in their own homes. Our main goal is to show the applicability of the entropy measures to time-series activity data analysis and to use the extracted measures as new engineered features that can be fed into inference and analysis models. The results of our experiments show that in most cases the combination of these measures can indicate the occurrence of healthcare-related events. We also find that different participants with the same events may have different measures based on one entropy measure. So using a combination of these measures in an inference model will be more effective than any of the single measures.
Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems
Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）；University of Electronic Science and Technology of China（电子科技大学）
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring
Global AI Accelerator, Ericsson（爱立信全球人工智能加速器）；Ericsson Research（爱立信研究院）
各种医疗保健应用，如辅助生活、跌倒检测等，需要通过人类活动识别（HAR）对用户行为进行建模。这样的应用需要使用机器学习技术来表征来自多个资源受限的用户设备的见解，以用于有效的个性化活动监视。基于设备的联邦学习被证明是分布式协作机器学习的一种有效方法。然而，在解决用户之间的统计（非IID数据）和模型异质性方面存在各种挑战。此外，在本文中，我们探索了一个新的挑战-在联邦学习过程中处理用户之间标签（活动）的异质性。为此，我们提出了一个框架，联邦标签为基础的聚合，利用重叠的信息增益跨活动使用模型蒸馏更新。我们还建议，模型分数的联邦传输是足够的，而不是从设备到服务器的模型权重传输。在Raspberry Pi 2上使用Heteroimmune Human Activity Recognition（HHAR）数据集（有四个活动用于有效阐明结果）进行的实证评估表明，平均确定性准确度至少提高了~ 11.01%，从而证明了我们提出的框架的设备上功能。
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring
Department of Computer Science（计算机科学系）；Technion—Israel Institute of Technology（技术ion—以色列理工学院）；Department of Information Systems（信息系统系）；University of Haifa（海法大学）
Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Florida State University（佛罗里达州立大学）；Bosch Research and Technology Center（博世研究技术中心）
COBRA: Multimodal Sensing Deep Learning Framework for Remote Chronic Obesity Management via Wrist-Worn Activity Monitoring
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK（帝国理工学院电子与电气工程系）；Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK（牛津大学生物医学工程研究所）
An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring
Department of Electrical and Computer Engineering, Faculty of Engineering and Computer Science, University of Victoria（维多利亚大学工程与计算机科学学院电气与计算机工程系）；Department of Mechanical Engineering, Faculty of Engineering and Computer Science, University of Victoria（维多利亚大学工程与计算机科学学院机械工程系）
RNNs on Monitoring Physical Activity Energy Expenditure in Older People
Leiden Institute of Advanced Computer Science, Universiteit Leiden（莱顿大学莱顿高级计算机科学研究所）；Molecular Epidemiology, Dept. Biomedical Data Science, LUMC, Leiden（莱顿大学医学中心生物医学数据科学系分子流行病学）；Data Science research group, University of Twente（特文特大学数据科学研究组）
FedStack: Personalized activity monitoring using stacked federated learning
University of Southern Queensland（南昆士兰大学）；Metro North Hospital and Health Service（北大都会医院与卫生服务局）；Royal Brisbane and Women’s Hospital（皇家布里斯班妇女医院）；Queensland University of Technology（昆士兰科技大学）；Singapore University of Social Sciences（新加坡社会科学大学）
Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment
Institute of Physics and Technology, Petrozavodsk State University（彼得罗扎沃茨克国立大学物理与技术学院）；Kuwait College of Science and Technology（科威特科学技术学院）；Universiti Malaysia Perlis（马来西亚玻璃市大学）；Institute of Mathematics and Information Technology, Petrozavodsk State University（彼得罗扎沃茨克国立大学数学与信息技术学院）
