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1. Examining the design, manufacturing and analytics of smart wearables

   https://doi.org/10.1002/mds3.10087  

   Lopez, Ximena ; Afrin, Kahkashan ; Nepal, Bimal ( June 2020 , MEDICAL DEVICES & SENSORS)

   Recent advancements in sensors, device manufacturing and big data technologies have enabled the design and manufacturing of smart wearables for a wide array of applications in health care. These devices can be used to remotely monitor and diagnose various diseases and aid in the rehabilitation of patients. Smart wearables are an unobtrusive and affordable alternative to costly and time‐consuming healthcare efforts such as hospitalization and late diagnosis. Developments in micro‐ and nanotechnologies have led to the miniaturization of sensors, hybrid 3D printing of flexible plastics, embedded electronics and intelligent fabrics, as well as wireless communication mediums that permit the processing, storage and communication of data between patients and healthcare facilities. Due to these complex component architectures that comprise smart wearables, manufacturers have faced a number of problems, including minimum sensor configuration, data security, battery life, appropriate user interfaces, user acceptance, proper diagnosis and many more. There has been a significant increase in interest from both the academic and industrial communities in research and innovation related to smart wearables. However, as smart wearables integrate several different aspects such as design, manufacturing and analytics, the existing literature is quite widespread, making it less accessible for researchers and practitioners. The purpose of this study was to narrow this gap by providing a state‐of‐the‐art review of the extant design, manufacturing and analytics literature on smart wearables—all in one place—thereby facilitating future work in this rapidly growing field of research and application. Lastly, it also provides an in‐depth discussion on two very important challenges facing the smart wearable devices, which include barriers to user adoption and the manufacturing technologies of the wearable devices.

    

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2. PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber–Physical Systems

   https://doi.org/10.3390/s24030938  

   Bathalapalli, Venkata K. ; Mohanty, Saraju P. ; Kougianos, Elias ; Iyer, Vasanth ; Rout, Bibhudutta ( February 2024 , Sensors)

   This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber–physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient’s Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle’s Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient’s gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient’s BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time. 

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3. A review of inkjet printing technology for personalized-healthcare wearable devices

   https://doi.org/10.1039/D2TC02511F  

   Du, Xian ; Wankhede, Sahil P. ; Prasad, Shishir ; Shehri, Ali ; Morse, Jeffrey ; Lakal, Narendra ( October 2022 , Journal of Materials Chemistry C)

   Personalized healthcare (PHC) is a booming sector in the health science domain wherein researchers from diverse technical backgrounds are focusing on the need for remote human health monitoring. PHC employs wearable electronics, viz. group of sensors integrated on a flexible substrate, embedded in the clothes, or attached to the body via adhesive. PHC wearable flexible electronics (FE) offer numerous advantages including being versatile, comfortable, lightweight, flexible, and body conformable. However, finding the appropriate mass manufacturing technologies for these PHC devices is still a challenge. It needs an understanding of the physics, performance, and applications of printing technologies for PHC wearables, ink preparation, and bio-compatible device fabrication. Moreover, the detailed study of the operating principle, ink, and substrate materials of the printing technologies such as inkjet printing will help identify the opportunities and emerging challenges of applying them in manufacturing of PHC wearable devices. In this article, we attempt to bridge this gap by reviewing the printing technologies in the PHC domain, especially inkjet printing in depth. This article presents a brief review of the state-of-the-art wearable devices made by various printing methods and their applications in PHC. It focuses on the evaluation and application of these printing technologies for PHC wearable FE devices, along with advancements in ink preparation and bio-compatible device fabrication. The performance of inkjet, screen, gravure, and flexography printing, as well as the inks and substrates, are comparatively analyzed to aid PHC wearable sensor design, research, fabrication, and mass manufacturing. Moreover, it identifies the application of the emerging mass-customizable printing technologies, such as inkjet printing, in the manufacturing of PHC wearable devices, and reviews the printing principles, drop generation mechanisms, ink formulations, ink-substrate interactions, and matching strategies for printing wearable devices on stretchable substrates. Four surface matching strategies are extracted from literature for the guidance of inkjet printing of PHC stretchable electronics. The electro-mechanical performance of the PHC FE devices printed using four surface matching strategies is comparatively evaluated. Further, the article extends its review by describing the scalable integration of PHC devices and finally presents the future directions of research in printing technologies for PHC wearable devices. 

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4. WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

   https://doi.org/10.1155/2018/8125126  

   Dargazany, Aras R. ; Stegagno, Paolo ; Mankodiya, Kunal ( November 2018 , Mobile Information Systems)

   This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things (IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent wearable technologies as edge devices for big data collection. In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers. Now, the ultimate challenges are (1) how to analyze the collected wearable big data without any background information and also without any labels representing the underlying activity; and (2) how to recognize the spatial/temporal patterns in this unstructured big data for helping end-users in decision making process, e.g., medical diagnosis, rehabilitation efficiency, and/or sports performance. Deep learning (DL) has recently gained popularity due to its ability to (1) scale to the big data size (scalability); (2) learn the feature engineering by itself (no manual feature extraction or hand-crafted features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data. In order to understand the current state-of-the-art, we systematically reviewed over 100 similar and recently published scientific works on the development of DL approaches for wearable and person-centered technologies. The review supports and strengthens the proposed bioinspired architecture of WearableDL. This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics. 

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5. A Survey of Patterns for Adapting Smartphone App UIs to SmartWatches

   https://doi.org/10.1145/3379503.3403564  

   Zhou, Zhilan ; Xu, Jian ; Balasubramanian, Aruna ; Porter, Donald E. ( January 2020 , 22nd International Conference on Human-Computer Interaction with MobileDevices and Services (MobileHCI ’20),)

   Wearable devices, such as smart watches and fitness trackers are growing in popularity, creating a need for application developers to adapt or extend a UI, typically from a smartphone, onto these devices. Wearables generally have a smaller form factor than a phone; thus, porting an app to the watch necessarily involves reworking the UI. An open problem is identifying best practices for adapting UIs to wearable devices. This paper contributes a study and data set of the state of practice in UI adaptation for wearables. We automatically extract UI designs from a set of 101 popular Android apps that have both a phone and watch version, and manually label how each UI element, as well as how screens in the app, are translated from the phone to the wearable. The paper identifies trends in adaptation strategies and presents design guidelines. We expect that the UI adaptation strategies identified in this paper can have wide-ranging impacts for future research and identifying best practices in this space, such as grounding future user studies that evaluate which strategies improve user satisfaction or automatically adapting UIs. 

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