The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Role of a 24-hour Ambulatory Internet of Things System in Preeclampsia Monitoring: Technologies, Challenges, and Future Path Survey

The Internet of Things (IoT) is a technology that integrates different sensor actuators, working together for data management towards efficient communication within the digital world. IoT has been applied in many sectors to achieve sustainable development goals. Massive devices and a huge amount of data have been the major components of the technology, which has presented new challenges. IoT has been applied in healthcare to improve several ways of managing health, including antenatal care. Worldwide, the cost of having preeclampsia monitoring has been a major concern. A 24-hour ambulatory IoT system, an integration of a smartwatch, a mobile device, and a cloud-based application, is one of the technologies used to help in preeclampsia monitoring. IoT and its functionalities have been evaluated in previous studies and assessments. However, they concentrated on its application in other areas, such as animal husbandry, and little on ambulatory care. The impact of a real-time ambulatory IoT system on preeclampsia monitoring are comprehensively and methodically examined in this paper, focusing on three categories: the challenges and its benefits in ambulatory care. The application’s effects, performance, and safety have been thoroughly described. Generally, this paper explores potential initiatives of the IoT system to address existing ambulatory care issues

The Application of Image Recognition and Machine Learning to Capture Readings of Traditional Blood Pressure Devices: A Platform to Promote Population Health Management to Prevent Cardiovascular Diseases

Digital solutions for Blood Pressure Monitoring (or Telemonitoring) have sprouted in recent years, innovative solutions are often connected to the Internet of Things (IoT), with mobile health (mHealth) platform. However, clinical validity, technology cost and cross-platform data integration remain as the major barriers for the application of these solutions. In this paper, we present an IoT-based and AI-embedded Blood Pressure Telemonitoring (BPT) system, which facilitates home blood pressure monitoring for individuals. The highlights of this system are the machine learning techniques to enable automatic digits recognition, with F1 score of 98.5%; and the cloud-based portal developed for automated data synchronization and risk stratification. Positive feedbacks on trial implementation are received from three clinics. The overall system architecture, development of machine learning model in digit identification and cloud-based telemonitoring are addressed in this paper, alongside the followed implications

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Optical heart rate measurement with Arduino MKR1000

Abstract. The primary goal of this thesis is to introduce the reader to the fundamentals of optical methods for cardiovascular monitoring. Opposed to traditional methods that rely on measuring the electrical activity of the heart with electrodes, optical methods utilize light and are thus considered indirect methods. The thesis begins with an introduction to photoplethysmography, a commonly used technique based on illuminating the skin and measuring the intensity of the reflected or transmitted light. In addition to presenting the operating principle of this method, some of the key issues and use cases are discussed; in particular, methods for estimating heart rate will be presented in more detail, including an example algorithm. Additionally, the core principles of the Arduino ecosystem and the WebSocket protocol will be considered. In the latter part of this thesis, an implementation of an Internet of Things -capable optical heart rate meter based on the Arduino MKR1000 will be presented. Finally, the drawbacks and benefits of both photoplethysmography and the implemented system will be discussed in brief.Optinen sykemittaus Arduino MKR1000 -laitteella. Tiivistelmä. Tämän kandidaatintyön tavoitteena on esitellä lukijalle sydän- ja verisuonijärjestelmän toiminnan mittaamiseen käytettävien optisten menetelmien pääperiaatteita. Toisin kuin perinteiset menetelmät, jotka perustuvat sydämen sähköisen toiminnan mittaamiseen elektrodien avulla, optiset menetelmät hyödyntävät valoa ja ovat siten epäsuoria menetelmiä. Työssä käsitellään yleisesti käytössä olevaa fotopletysmografiaa, joka perustuu ihon valaisemiseen ja heijastuneen tai läpäisseen valon intensiteetin mittaamiseen. Fotopletysmografian toimintaperiaatteen esittelemisen lisäksi käsitellään joitakin tärkeimpiä käyttökohteita ja menetelmän haasteita. Tarkemmin käsitellään fotopletysmografiaan perustuvan sykemittauksen toimintaperiaate ja esitellään esimerkkialgoritmi. Tämän jälkeen käsitellään pääpiirteittäin Arduino-ekosysteemiä ja WebSocket-protokollaa. Työn jälkimmäisessä osassa esitellään esineiden Internet -käyttöön soveltuva toteutus optisesta sykemittarista. Toteutukseen käytetään Arduino MKR1000 -kehitysalustaa ja hyödynnetään WebSocket-protokollaa. Lopuksi pohditaan lyhyesti sekä fotopletysmografian että toteutetun sykemittarin ansioita, haasteita ja jatkokehitysmahdollisuuksia

Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages. © The Author(s) 2023

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Remote vital signs monitoring based on wireless sensor networks

Tese de doutoramento em Líderes para as Indústrias TecnológicasGovernmental and private institutions face a major challenge to provide quality health care to a population consisting of a growing number of elderly and chronically ill patients. According to the World Health Organization, in 2006, the total global health expenditures exceeded US$ 4 trillion and are rising in the majority of countries including Portugal which, during 2006, expended 9.9% of its gross domestic product in health care. The use of remote vital signs monitoring systems increases the probability of early detection of risky situations, allows frequent monitoring of in-patients, elderly and chronically ill patients, and streamlines the work of health professionals. However, at present, these systems are expensive, complex and employ obtrusive sensors, which limit their application to intensive care units and cardiac intermediate care units. This work is part of a project that aims to design, prototype and evaluate a remote vital signs monitoring system based on the IEEE 802.15.4 and ZigBee protocols, which allow the development of small low-power sensors. The prototype system comprises electrocardiogram/heart rate and axillary thermometer sensors, networking devices and three informatics applications that collect, process, and exhibit medical data. The wireless sensors, the networking devices and one of the applications were developed under this work. Additionally, the wireless sensor network was evaluated through simulations at the MAC level and experimental and field tests. Field tests were performed at an in-patient floor of Hospital Privado de Guimarães, a Portuguese hospital. Finally, questionnaires were used to measure the satisfaction of users and catalog their critics and suggestions for improvement. Simulations considered different topologies, operation modes and a crescent number of sensors and hops. Experimental and field tests confirmed most of the results obtained by simulations, but revealed that networks which did not assign transmission time slots to electrocardiogram sensors were unable to maintain a high delivery ratio. Contention between devices, aggravated by the inability of routers in receiving incoming packets during backoff, and collisions between packets generated by hiddennodes were responsible for most message losses. On the other hand, beacon-enabled star IEEE 802.15.4 networks that assigned a guaranteed time slot to sensors were able to maintain a very high delivery ratio. In contrast, these networks are restricted in terms of the coverage area and the number of sensors. Also, field tests showed that under low traffic scenarios ZigBee nonbeacon-enabled networks can achieve a high delivery ratio even in presence of a high percentage of hidden-nodes.Instituições governamentais e privadas enfrentam um grande desafio para prestar cuidados de saúde de qualidade a uma população constituída por um número crescente de idosos e doentes crónicos. Segundo a Organização Mundial de Saúde, em 2006, a despesa mundial em saúde ultrapassou a quantia de 4 bilhões de dólares americanos e cresce anualmente na maioria dos países, incluindo Portugal, o qual, em 2006, gastou 9,9% do seu produto interno bruto em cuidados de saúde. O uso de sistemas de monitorização remota de sinais vitais aumenta a probabilidade de deteção precoce de situações de risco, permite que doentes internados, idosos ou doentes crónicos sejam frequentemente monitorizados e agiliza o trabalho dos profissionais de saúde. No entanto, atualmente, estes sistemas são caros e complexos, o que limita a sua aplicação a alguns setores dos hospitais, tais como as unidades de cuidados intensivos e as unidades de cuidados intermédios na área da cardiologia. O projeto no qual insere-se este trabalho visa a conceção, a prototipagem e a avaliação de um sistema de monitorização remota de sinais vitais com base nos protocolos IEEE 802.15.4 e ZigBee, os quais oferecem a possibilidade de construção de sensores com consumos energéticos muito baixos e reduzidas dimensões. O sistema consiste em sensores de eletrocardiograma/frequência cardíaca e temperatura axilar, dispositivos de rede e três aplicações que coletam, processam e apresentam o eletrocardiograma e os sinais vitais. No âmbito deste trabalho foram desenvolvidos os sensores sem fios, os dispositivos de rede e uma das aplicações informáticas. Além disso, foi feita a avaliação do desempenho da rede de sensores sem fios através da análise de simulações a nível da camada de acesso ao meio (MAC) e de testes de laboratório e de campo. Os testes de campo da rede de sensores sem fios foram executados em um dos pisos de internamento do Hospital Privado de Guimarães. Finalmente, foram usados questionários para medir a satisfação dos utilizadores e recolher críticas e sugestões de melhoria. As simulações consideraram diferentes topologias e modos de operação, além de um número crescente de sensores e saltos. Testes experimentais e de campo confirmaram grande parte dos resultados obtidos por simulação mas, adicionalmente, revelaram que as redes constituídas por vários sensores de eletrocardiograma e que não reservaram um intervalo de tempo de transmissão aos sensores não foram capazes de manter uma elevada taxa de entrega de mensagens. Perdas de mensagens ocorreram devido a disputas entre sensores pelo acesso ao canal sem fios e devido a ocorrência de colisões de pacotes transmitidos por nós escondidos. Por outro lado, as redes baseadas no protocolo IEEE 802.15.4 que atribuíram um intervalo de tempo de transmissão a cada sensor conseguiram manter uma elevada taxa de entrega. Entretanto, essas redes são limitadas em termos da área de cobertura e do número de sensores. Adicionalmente, durante os testes de campo em cenários de tráfego reduzido, as redes ZigBee que não empregaram beacons atingiram uma elevada taxa de entrega mesmo na presença de uma grande percentagem de nós escondidos

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system