A Validation Study to Confirm the Accuracy of Wearable Devices Based on Health Data Analysis

This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management.

The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19.

Stroke patients under the background of the new crown epidemic need to be home-based care. However, traditional nursing methods cannot take care of the patients' lives in all aspects. Based on this, based on machine learning algorithms, our work combines regression models and SVM to build a smart wearable device system and builds a system prediction module to predict patient care needs. The node is used to collect human body motion and physiological parameter information and transmit data wirelessly. The software is used to quickly process and analyze the various motion and physiological parameters of the patient and save the analysis and processing structure in the database. By comparing the results of nursing intervention experiments, we can see that the smart wearable device designed in this paper has a certain effect in stroke care.

The Accuracy of Wearable Photoplethysmography Sensors for Telehealth Monitoring: A Scoping Review.

Background and Objectives: Photoplethysmography (PPG) sensors have been increasingly used for remote patient monitoring, especially during the COVID-19 pandemic, for the management of chronic diseases and neurological disorders. There is an urgent need to evaluate the accuracy of these devices. This scoping review considers the latest applications of wearable PPG sensors with a focus on studies that used wearable PPG sensors to monitor various health parameters. The primary objective is to report the accuracy of the PPG sensors in both real-world and clinical settings. Methods: This scoping review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Studies were identified by querying the Medline, Embase, IEEE, and CINAHL databases. The goal was to capture eligible studies that used PPG sensors to monitor various health parameters for populations with a minimum of 30 participants, with at least some of the population having relevant health issues. A total of 2,996 articles were screened and 28 are included in this review. Results: The health parameters and disorders identified and investigated in this study include heart rate and heart rate variability, atrial fibrillation, blood pressure (BP), obstructive sleep apnea, blood glucose, heart failure, and respiratory rate. An overview of the algorithms used, and their limitations is provided. Conclusion: Some of the barriers identified in evaluating the accuracy of multiple types of wearable devices include the absence of reporting standard accuracy metrics and a general paucity of studies with large subject size in real-world settings, especially for parameters such as BP.

Integrated Cloud Based System Using Wareable Devices for Health and Safety at Work

The development of IT and its acceptance in all fields of activity rose new challenges and possibilities, regarding the improvement of health state using wearable devices. The COVID-19 pandemic accelerated the IT adoption for working at distance, e-working or working at home. Such, the workers care under the envision of professional diseases prevention and dangerous situation avoidance can be enhanced by using mHealth solutions. The present article proposes a cross-platform design of an integrated system using wearables devices as sensors, for monitoring the health condition, dangerous situations, and physical effort of worker during work time, with various user categories, emergency squads, medical care, ergonomist, management, and workers.

Applicability of edge computing paradigm for Covid-19 mitigation

With lockdowns and overburdened medical facilities during the Covid-19 pandemic, technology and computing paradigms play a vital role in providing remote healthcare solutions. We assess as how the existing computing paradigms could be deployed to prevent the spread of the disease, expedite the diagnosis, and facilitate remote monitoring of patients to reduce the burden on the overstretched medical facilities. The chapter will include a literature survey based on the articles published in but not limited to Science Direct, Google Scholar, Research Gate, and PubMed. This study weighs the pros and cons of using different paradigms in diverse scenarios and provides recommendations for efficient healthcare solutions. The chapter also focuses on the issues related to edge computing, such as resource provisioning, energy preservation, etc. In this era of technology, edge computing can be used to enhance the efficacy of healthcare solutions without burdening healthcare professionals and facilities. In this chapter, experimentation will focus on deploying intelligent techniques in the edge computing paradigm. © 2023 Elsevier Inc. All rights reserved.

The profile of the patient to be ready to use digital technologies and Artificial Intelligence methods when receiving medical care

Introduction. During the COVID-19 pandemic, there was quarantine, limited contacts, and an increased burden on the healthcare system in the last two years. These problems have led to a rethinking and transformation of patients' readiness for the digitalisation of healthcare. Purpose. To form a patient profile, ready to use digital technologies and artificial intelligence methods in medical care during the COVID-19 pandemic, based on technological competence and digital literacy skills analysis. Material and methods. The sociological survey of patients was used through the remote distribution of links to the Google form on the Internet. The survey consists of 11 blocks, including an assessment of attitudes towards digital technologies and artificial intelligence in healthcare. Results. The average age of respondents was 41.8 ± 0.7 years, mostly female 225 (74%) in the group of patients ready to use electronic wearable devices to monitor and control their health. One hundred thirty-one people (43.1 %) regularly monitor their blood pressure levels. One hundred thirty-seven people (45%) assess their health as good and 133 (43.7%) satisfactory. 256 (84.2%) respondents mostly work full-time. Ones do physical exercises regularly in 34.2% (n = 104) cases and rarely in 48,7% (n = 148). Only 164 respondents (29.4%) consider it possible to use artificial intelligence methods in providing medical care, preventing the development of diseases and promoting a healthy lifestyle, against 256 people (45.9%), the remaining 137 people (24.6%) found it difficult to answer. Women (49.7%) were more often against artificial intelligence methods than men (33.6%). Conclusion. It is necessary to consider the patient's profile characteristics, who is ready to use digital technologies and artificial intelligence methods in medical care when developing effective programs to increase the level and pace of healthcare's digitalisation in the region. © AUTHORS, 2022.

Low-Cost Wearable Device to Provide an Effective Solution for Contact Tracing and Logistics

The paper introduces a low-cost wearable band that does the tedious, repetitive task of entering your required details in any shop or organization, as well as keeping a record of all the people you have come in contact with. There are two aspects of our device:1)If a person enters a shop with our device, the band will transmit the required information of the wearer to the reader kept at the shopkeeper's side wirelessly. The transmitted information will include the wearer's information (as per government guidelines) masked in the band's Unique ID along with their temperature status (whether having a temperature above 100°F or not).2)When two persons come near each other over a distance of 6 feet, the unique ID broadcasted from each other's bands gets stored in the other's band. If any of them tests positive for Coronavirus Disease (COVID-19) or similar diseases, his/her unique ID can be used to trace primary contacts and take appropriate steps to contain further spread.Privacy is key! So, we are reengineering the primary concept of contact tracing and logistics while keeping the user's information safe and secure. © 2023 IEEE.

Software Implementation of a Smart Bracelet Prototype to Monitor Vital Signs, Locate, and Track COVID-19 Patients in Quarantine Zone

This paper describes the design and software implementation of a wearable prototype that allows users to monitor the vital signs of COVID-19 patients in quarantine areas. This prototype consists of two parts, the bracelet, andthe Base control unit (BCU). The bracelet is built with ESP8266 and sensors as main components, as well as the battery and other parts needed to fulfill the system's purpose (monitoring the vital signs of COVID-19 patients). At the same time, the Raspberry Pi (SCB) single board computer and GSM/GPRS/HAT are the main components of the Basic Control Unit (BCU). The current work describes the main parts of the pseudocode, as well as the activity diagram for the microcontroller and Raspberry Pi. This paper describes the mechanism of sending alert messages, whereby the system's ability to configure two types of alert messages;(1) physician Messages (these Messages will be sent to the physicianassociated with the patient if one or more vital signs reach a critical value;these messages contain all measurements of a patient's vital signs);(2) Authorize messages (these messages will be sent if the quarantine rules are violated;the patient's location will be sent to the authorized person as a Google Mapslink). Also, this paper describes the graphical user interface for communication, management,. and interaction between the users of the system. © 2022 IEEE.

Design of real-time ECG monitoring wearable equipment based on the Holter system

The COVID-19 epidemic has largely restricted the traditional offline medical treatment model. In this study, we designed ECG monitoring smart clothing based on the Holter system after identifying and analyzing the needs of patients and doctors. This clothing is a wearable device that integrates monitoring and remote diagnosis, building a general network platform to realize remote data transfer sharing and online interactive auxiliary diagnosis. Wearable clothing that can monitor ECG in real time is designed and developed by intelligently integrating limb lead wires, conductive fiber fabrics, lead interfaces, and electrode signal storage receivers by using the human body sensing conduction principle of real-time ECG monitoring. Wearable real-time ECG monitoring clothing can help patients achieve fast virtual medical care and auxiliary diagnosis, and solve the design issues with electrode signal storage receivers. © 2022 IEEE.

Analyzing Wearable Data for Diagnosing COVID-19 Using Machine Learning Model

Health is an indispensable part of human life, but we realize its importance when we face health issues. Technology can play an important role in the healthcare sector. During the COVID-19 pandemic, many countries used technology to control the situation. Internet of Things-based wearable devices can change the whole scenario of diagnosing the disease. The physiological features collected using wearables can be used for pre-symptomatic prediction of disease. In this study, from the cohort of 185 participants, data of 36 participants are analyzed to predict COVID-19 before symptoms begin using the machine learning model. Our findings suggest that heart rate, BPM, SDNN, and steps features can be used to detect the COVID-19 before the symptoms appear. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Biosignal Databases for Training of Artificial Intelligent Systems

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will have mild to moderate respiratory diseases, however, the elderly population is the most vulnerable, becoming seriously ill, requiring continuous medical follow-up. In this sense, technologies were developed that allow continuous and individual monitoring of patients, in a home environment, namely through wearable devices, thus avoiding continuous hospitalization. Thus, these devices allow great improvements in data analysis methods since they can continuously acquire the physiological signals of an individual and process them in real-Time through artificial intelligence (AI) methods. However, training of AI methods is not straightforward, requiring a large amount of data. In this study, we review the most common biosignal databases available in the literature. A total of thirteen databases were selected. Most of the databases (9 databases) were related to ECG signal, as well as 4 databases containing signals from SPO2, Heart Rate, Blood Pressure, etc. Characteristics were described, namely: The population of the databases, data resolution, sampling rates, sample time, number of signal samples, annotated classes, data acquisition conditions, among other aspects. Overall, this study summarizes and described the public biosignals databases available in the literature, which may be important in the implementation of intelligent classification methods. © 2022 ACM.

Promoting smart wearable devices in the health-AI market: the role of health consciousness and privacy protection: An International Journal

PurposeConsumers interacting with smart wearable devices is on the rise in the current health-AI market, which offers a great opportunity for companies to execute interactive marketing. However, this opportunity is mainly reliant on consumers' use of smart wearable devices. This paper aims to develop a model considering health and privacy factors to elucidate consumers' use of smart wearable devices for unleashing their full potential in interactive marketing.Design/methodology/approachThe authors collected 250 samples via an online survey to validate the smart wearable devices usage model that elucidates factors that stimulate consumer usage, including privacy concerns, health consciousness and consumer innovativeness. The authors used structural equation modeling and multi-group analysis to test the hypotheses.FindingsPrivacy concerns of consumers have a negative effect on smart wearable devices usage, while health consciousness positively impacts consumers' usage of smart wearable devices. Consumer innovativeness indirectly affects smart wearable devices usage via effort expectancy. Experienced consumers are less sensitive to the performance expectancy but more affected by effort expectancy regarding smart wearable devices.Originality/valueThe present study contributes to the literature stream of health-AI usage by unraveling the impacts of privacy concerns and health consciousness and examining the moderating role of prior experience. The findings suggest marketers in the health-AI industry should endeavor to build transparent and sound privacy protection mechanisms and promote smart wearable devices by fostering health awareness of potential consumers.

An Insight into Wearable Devices for Smart Healthcare Technologies

Physical fitness is the prime priority of people these days as everyone wants to see himself as healthy. There are numbers of wearable devices available that help human to monitor their vital body signs through which one can get an average idea of their health. Advancements in the efficiency of healthcare systems have fueled the research and development of high-performance wearable devices. There is significant potential for portable healthcare systems to lower healthcare costs and provide continuous health monitoring of critical patients from remote locations. The most pressing need in this field is developing a safe, effective, and trustworthy medical device that can be used to reliably monitor vital signs from various human organs or the environment within or outside the body through flexible sensors. Still, the patient should be able to go about their normal day while sporting a wearable or implanted medical device. This article highlights the current scenario of wearable devices and sensors for healthcare applications. Specifically, it focuses on some widely used commercially available wearable devices for continuously gauging patient's vital parameters and discusses the major factors influencing the surge in the demand for medical devices. Furthermore, this paper addresses the challenges and countermeasures of wearable devices in smart healthcare technology. © 2023 IEEE.

AI-Enabled Wearable Medical Internet of Things in Healthcare System: A Survey

Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems. © 2023 by the authors.

Real-Time Monitoring and Early Warning of a Cytokine Storm In Vivo Using a Wearable Noninvasive Skin Microneedle Patch.

A cytokine storm may be the last attack of various diseases, such as sepsis, cancer, and coronavirus disease 2019, that can be life threatening. Real-time monitoring of cytokines in vivo is helpful for assessing the immune status of patients and providing an early warning of a cytokine storm. In this study, a functional carbon nanotube biointerface-based wearable microneedle patches for real-time monitoring of a cytokine storm in vivo via electrochemical analysis are reported. This wearable system has sensitivity with a detection limit of 0.54 pg mL-1 , high specificity, and 5 days of stability with a coefficient of variation of 4.0%. The system also has a quick response of several hours (1-4 h) to increasing cytokines. This wearable microneedle patch may offer a promising route for real-time biomolecule wearables construction. The patch is also the first reported integrated capture and monitoring system that is capable of real-time measurement of protein markers in interstitial fluid.

Validation of Oura ring energy expenditure and steps in laboratory and free-living.

BACKGROUND: Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living. METHODS: Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count. RESULTS: Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits. CONCLUSION: This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.

Blockchain in Healthcare: 2023 Predictions From Around the Globe.

Over the past 50 years, although categorized as the "Information Age" or "Digital Age," the vast amounts of digitized data have been sorely underutilized. Only recently, in response to the COVID-19 pandemic, efforts have accelerated to harness these data using blockchain technology as it pertains to healthcare. Today, through the blockchain infrastructure and its tokenization applications, we are able to leverage healthcare data effectively into more efficient business processes. In addition, we can secure better patient engagement and outcomes, while generating new revenue streams for an array of healthcare stakeholders. It is in the application of blockchain technology to compile these stockpiled data into new, compliant business models that we can reap the full potential of the blockchain. Here are predictions by members of the BHTY editorial board members on how we might further advance the role of blockchain in healthcare in 2023.

Wearable devices for anxiety & depression: A scoping review.

Background: The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices. Objective: This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression. Methods: Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized. Results: From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (n = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device. Conclusion: The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research. • R tutorial to quantify resilience from physical activity time series. • Physical activity resilience is measured using an idiographic approach. • Physical activity resilience is operationalized as the AUC. • Growth models are fitted to step count time series to define the limits of the AUC. • Further indicators of resilience are provided to describe the phenomenon. [ FROM AUTHOR]

Consumer interaction with cutting-edge technologies: Implications for future research

This article provides an overview of extant literature addressing consumer interaction with cutting-edge technologies. Six focal cutting-edge technologies are identified: artificial intelligence, augmented reality, virtual reality, wearable technology, robotics and big data analytics. Our analysis shows research on consumer interaction with cutting-edge technologies is at a nascent stage, and there are several gaps requiring attention. To further advance knowledge, our article offers avenues for future interdisciplinary research addressing implications of consumer interaction with cutting-edge technologies. More specifically, we propose six main areas for future research namely: rethinking consumer behaviour models, identifying behavioural differences among different generations of consumers, understanding how consumers interact with automated services, ethics, privacy and the blackbox, consumer security concerns and consumer interaction with new-age technologies during and after a major global crisis such as the COVID-19 pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved)