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. This paper studies the novel problem of adaptive energy management towards the goal of self-sustainable wearables by using harvested energy to supplement the battery energy and to reduce manual recharging by users. To solve this problem, we propose a principled algorithm referred as AdaEM. There are two key ideas behind AdaEM. First, it uses machine learning (ML) methods to learn predictive models of user activity and energy usage patterns. These models allow us to estimate the potential of energy harvesting in a day as a function of the user activities. Second, it reasons about the uncertainty in predictions and estimations from the ML models to optimize the energy management decisions using a dynamic robust optimization (DyRO) formulation. We propose a light-weight solution for DyRO to meet the practical needs of deployment. We validate the AdaEM approach on a wearable device prototype consisting of solar and motion energy harvesting using real-world data of user activities. Experiments show that AdaEM achieves solutions that are within 5% of the optimal with less than 0.005% execution time and energy overhead.
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）
Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector
The Chinese University of Hong Kong（香港中文大学）；Institute of Future Cities, The Chinese University of Hong Kong（香港中文大学未来城市研究所）；Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong（香港中文大学李嘉诚健康科学研究所）
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）
An Energy Efficient Wearable for Monitoring Elderly People Health
Graduate Program in Computer Science at Pontifical Catholic University of Rio Grande do Sul (PPGCC/PUCRS)（南里奥格兰德天主教大学计算机科学研究生项目）；Graduate Program in Biomedical Gerontology at Pontifical Catholic University of Rio Grande do Sul (GERONBIO/PUCRS)（南里奥格兰德天主教大学生物医学老年学研究生项目）
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）
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）
