In our recent book Health-e Everything: Wearables and the Internet of Things for Health, we capture in an interactive e-book format some global thought-leader perspectives as well as early examples of case studies and novel innovations that are driving this emerging technology domain. Here, we provide a brief snapshot of key findings related to these novel technologies and use cases, which are driving both health care practitioners and health consumers (patients). As technologists, having a firm understanding of customer-driven innovation and the actual user benefits of interconnective devices for health will help us engineer better solutions that are more targeted to the triple aim of better, faster, and cheaper health solutions.
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http://dx.doi.org/10.1109/MPUL.2016.2592260 | DOI Listing |
Comput Biol Med
January 2025
Department of Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Electronic address:
Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks.
View Article and Find Full Text PDFPolymers (Basel)
December 2024
Department of Electronic Engineering, Institute for Wearable Convergence Electronics, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin 17104, Republic of Korea.
An energy crisis, resulting from rapid population growth and advancements in the Internet of Things, has increased the importance of energy management strategies. Conventionally, energy management is conducted using sensors; however, additional energy is required to maintain sensor operation within these systems. Herein, an all-fiber-based triboelectric nanogenerator with O plasma treatment, graphene oxide/tannic acid solution coating, and hexane/1-octadecanethiol solution coating (AFT-OGH) is fabricated to implement a self-powered sensor, generating a high electrical power density, of 0.
View Article and Find Full Text PDFF S Rep
December 2024
Northwell, New Hyde Park, New York.
Devices that function within a network of interconnected systems and are equipped with sensors, software, and tools designed to collect and exchange data are widely known as the Internet of Things (IoT). In recent years, the rapid growth of IoT technology has sparked significant interest in leveraging these systems to enhance healthcare delivery across various medical fields, including fertility care and assisted reproductive technology. The subset of IoT devices applied within the healthcare sector is referred to as the Internet of Medical Things (IoMT).
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China.
Human-machine interfaces and wearable electronics, as fundamentals to achieve human-machine interactions, are becoming increasingly essential in the era of the Internet of Things. However, contemporary wearable sensors based on resistive and capacitive mechanisms demand an external power, impeding them from extensive and diverse deployment. Herein, a smart wearable system is developed encompassing five arch-structured self-powered triboelectric sensors, a five-channel data acquisition unit to collect finger bending signals, and an artificial intelligence (AI) methodology, specifically a long short-term memory (LSTM) network, to recognize signal patterns.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
FORTH-ICS, Heraklion, Greece.
Background: Patients undergoing surgery often experience stress and anxiety, which can increase complications and hinder recovery. Effective management of these psychological factors is key to improving outcomes. Preoperative anxiety is inversely correlated with the amount of information patients receive, but accessible, personalized support remains limited, especially in preoperative settings.
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