Investigation on Monitoring Personal Health via Internet of Things-Based Wearable Device

Heart failure (HF) is the fastest growing cardiovascular condition globally; associated management costs and hospitalizations place an immense burden on healthcare systems. Wearable electronic devices (WEDs) may be useful tools to enhance HF management and mitigate negative health outcomes. We aimed to perform a systematic review to examine the potential of WEDs to support HF self-care in ambulatory patients at home. Five databases were searched for studies published between 2007 and May 2022, including OVID MEDLINE, EMBASE (OVID), APA PsycINFO (OVID), Cochrane Central Register of Controlled Trials (OVID), and CINAHL Plus with Full Text (Ebsco). After 6210 duplicates were removed, 4045 records were screened and 6 were included for review (2 conference abstracts and 4 full-text citations). All studies used WEDs as 1 component of a larger intervention. Outcome measures included quality of life, physical activity, self-efficacy, self-care, functional status, time to readmission, social isolation, and mood. Studies were of moderate to high quality and mixed findings were reported. Enhanced exercise habits and motivational behavior to exercise, as well as decreased adverse symptoms of fatigue and dyspnea, were identified in 2 studies. However, improvements in exercise capacity and increased motivational behavior did not lead to exercise adherence in another 2 studies. The findings from this review suggest that WEDs may be a viable health behavior improvement strategy for patients with HF. However, studies of higher quality, with the primary intervention being a WED, and consistent outcome measures are needed to replicate the positive findings of studies identified in this review.

Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.

Factors influencing adoption model of continuous glucose monitoring devices for internet of things healthcare

Wearable devices are the significant ubiquitous technology of the Internet of Things in day-to-day life. The efficient data processing in various devices such as smart clothes, smart wristwear and medical wearables along with consumer-oriented service of the IoT technology becomes inevitable in smart healthcare systems. The wearable market is currently dominated by health, safety, interaction, tracker, identity, fitness etc. Wearables increase the convergence of physical and digital world which automatically bring people into the IoT. The popularity of wearable devices is growing exponentially since it entirely changes the way how the consumers interact with the environment. 74% people believe that the wearable sensors assist them in interacting with the physical objects around them. Henceforth, one out of three smartphone users will wear minimum 5 wearables in 2020. Moreover, 60% believe that wearables in the next five years will be used not only to track health related information, although it can be used to control objects, unlock doors, authenticate identity and transactions. Wearables must be evolved to cope with the future to meet the expectations of consumers, where the users will wear many devices that is connected with the internet to interact with the physical surroundings and receive data in a seamless secure way. By 2021, smartwatches are estimated to be sold to nearly 81 million units which signifies 16% sales of total wearable device. According to the latest figure of Gartner report, the global shipment of wearable devices are anticipated to raise by 25.8% every year to $225 million (GBP 176.3 million) in 2019. Researchers also forecasted that the usage of wearable devices by the end users will increase to $42 billion (GBP 32.9 million) in 2019. In recent years, the IoT based Smart Healthcare system has influenced greatly on growing demand of wearable devices. In fact, the Wearable IoT (WIoT) devices are generating huge volume of personal health data. Enabling technologies such as cloud computing, Fog computing and Big Data play vital role in leveraging WIoT services. These enabling services over the voluminous health data enhance clinical process at health care system at remote or local servers. The traditional remote healthcare information system involves data transfer, signal processing mechanism and naive machine learning models deployed on remote server to process the medical data of patients. This technique has several demerits like they are not suitable for resource constrained wearable IoT devices. The resources such as processing, memory, energy, networking capability are limited in WIoT devices. Traditional mechanism lacks optimization of resource usage, prediction of medical condition, and dynamic assessment based on available information. Further, the naive machine learning techniques does not perform knowledge generation, decision making and discover hidden valuable patterns from the available medical data. The integrated platform in which cloud computing serves as backend computing systems, Fog computing as edge computing and Big data as platform for data analysis, knowledge generation promise to provide valid solution to several issues of Wearable IoT devices. Next, the health data generated through WIoT devices are personal and sensitive. Hence, the security and privacy of such delicate data at all level of WIoT ecosystem is essential. This part of chapter will contribute towards understanding the recent research work, issues, challenges, and opportunities in applying enabling technologies for WIoT. Also, how well the security and privacy can be incorporated is also discussed.

The popularity of wearable devices equipped with a variety of sensors that can measure users’ health status and monitor their lifestyle has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data from users. Although the health data collected via wearable devices are useful for providing healthcare services, the indiscriminate collection of an individual’s health data raises serious privacy concerns. This is because the health data measured and monitored by wearable devices contain sensitive information related to the wearer’s personal health and lifestyle. Therefore, we propose a method to aggregate health data obtained from users’ wearable devices in a privacy-preserving manner. The proposed method leverages local differential privacy, which is a de facto standard for privacy-preserving data processing and aggregation, to collect sensitive health data. In particular, to mitigate the error incurred by the perturbation mechanism of location differential privacy, the proposed scheme first samples a small number of salient data that best represents the original health data, after which the scheme collects the sampled salient data instead of the entire set of health data. Our experimental results show that the proposed sampling-based collection scheme achieves significant improvement in the estimated accuracy when compared with straightforward solutions. Furthermore, the experimental results verify that an effective tradeoff between the level of privacy protection and the accuracy of aggregate statistics can be achieved with the proposed approach.

Fitness Wearables

Regulatory regimes and procedural values for health-related motion data in the United States and Canada

Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.

The proliferation of wearable technology has transformed personal fitness and athletic performance. Tracing the development from early pedometers to AI-driven smart wearables, this chapter examines how these technologies have empowered individuals to continuously monitor personal health and elite athletic performance. Wearable technologies enable real-time tracking of metrics such as heart rate, sleep, and energy expenditure, refining both performance optimization and everyday health routines. In elite sport, wearables aid in injury prevention, workload management, and decision-making, while in recreational contexts, they promote active leisure through gamified engagement and behavior tracking. This chapter explores the evolution, functions, and implications of wearable technology, addressing core concepts, ethical concerns, and design trends. By integrating case-based insights and technological developments, this chapter offers a comprehensive view of how wearable technologies enhance motivation, accountability, and wellness in sport and personal health management.

Enerji teknolojik ve ekonomik kalkınma için önemli bir faktördür. Artan çevre bilinciyle birlikte elektronik cihazların hızlı gelişiminin neden olduğu büyük enerji tüketimi, yeşil ve yenilenebilir enerjiyi üretmek ve depolamak için yeni teknoloji gereksinimlerini arttırmıştır. Yenilenebilir enerji kaynakları arasında güneş enerjisi gün geçtikçe önem kazanmaktadır. Çünkü güneş enerjisi dünyadaki en bol, sürdürülebilir ve en temiz enerjidir. Işıktan elektrik üretim teknolojisinin sürekli gelişmesiyle birlikte, güneş enerjisinin diğer konvansiyonel enerjiler içindeki payı gittikçe artmakta ve fosil yakıtlara alternatif haline gelmektedir. Geliştirilen yeni teknolojiler ile güneş enerjisi değişik alanlarda kullanılmaktadır. Bu bağlamda, kendi gücünü sağlayan enerji teknolojisi, elektronik cihazların harici güç kaynağı olmadan sürekli çalışmasını sağlayabildiğinden, gelecekteki giyilebilir elektronikler için oldukça umut vericidir. Günümüzde farklı tipteki esnek güneş hücreleri (EGH’ler) kullanılarak kendi gücünü sağlayan giyilebilir elektronik teknolojiler geliştirilmektedir. Giyilebilir elektronikler son yıllarda büyük ilgi görmekte ve hızlı bir büyüme yaşamaktadır. Bu teknolojiler daha çok eğlence, akıllı izleme, kişisel sağlık ve egzersiz kontrolü amacıyla kullanılmaktadır Bu çalışmada, esnek güneş hücreleri ve bu hücreler kullanılarak geliştirilen kendi gücünü sağlayan giyilebilir elektronik teknolojiler özetlendi. Bu bağlamda, öncelikle, esnek silikon güneş hücreleri (ESGH'ler), esnek perovskit güneş hücreleri (EPGH'ler), esnek organik güneş hücreleri (EOGH 'ler) ve esnek boya duyarlı güneş hücreleri (EBDGH 'ler) ele alındı. Daha sonra esnek güneş hücrelere entegre kendi gücünü sağlayan giyilebilir enerji teknolojilerinden ter izleme, hareket izleme, giyilebilir kumaş, nabız izleme, gaz sensörü ve giyilebilir ekran sistemleri tanıtıldı. Son olarak giyilebilir teknolojilerin ve EGH’lerin önündeki zorluklar ve çözüm yolları ile gelecekteki durumları ile ilgili öngörüler sunuldu.

This paper explores the utilization of physiological signals, including heart rate variability (HRV) and electroencephalography (EEG), in emotion recognition through wearable devices. Heart Rate Variability (HRV) is closely linked to emotional arousal. HRV can detect subtle changes in heart rate patterns, which are indicative of different emotional states. By analyzing these patterns, researchers can identify and differentiate between various emotions someone may be experiencing. The integration of heart sound signals alongside traditional ECG signals presents an innovative approach, enhancing the accuracy of emotion recognition systems. Similarly, EEG rhythms are investigated for their association with cognitive and emotional states. This involves using brain rhythm sequences in classification tasks. The study underscores the significance of single-channel selection in EEG-based emotion recognition, demonstrating notable improvements in accuracy. Furthermore, wearable emotion recognition devices offer potential benefits for personalized emotion management and mental health intervention, catering to individuals with affective disorders and aiding in medical diagnosis and treatment. The Smartex S.R.L. platform exemplifies the advancement in wearable monitoring technology, facilitating data acquisition and interpretation for emotion recognition, particularly in patients with bipolar disorder. Overall, the paper highlights the evolving landscape of emotion recognition technology, with HRV and EEG emerging as prominent techniques alongside advancements in wearable device design and signal processing methodologies.

BackgroundSerious mental illness (SMI) is difficult to treat for various reasons, such as rapid changes in symptoms, comorbid health conditions, long gaps between provider visits, and additional societal barriers experienced by this population. Wearable mobile-sensing devices can be used to passively collect valuable patient-generated health data, such as daily step count, heart rate variability, sleep information, and other health-related behaviors, which could inform and improve treatment for individuals with SMI. Wearable health devices have become more economically accessible, providing promise for the possibility of their implementation in health care. However, more information regarding how individuals with SMI perceive and interact with these devices is needed.ObjectiveThis study aimed to assess the acceptability and feasibility of using wearable mobile-sensing devices to improve treatment outcomes for Veterans with SMI. In addition, we were also interested in learning if privacy concerns would influence acceptability of devices, specifically surrounding location tracking and health information sharing, as well as assessing other barriers to device use.MethodsQualitative interviews were conducted with participants who had been using a wearable health and fitness tracker for at least 2 weeks to explore their thoughts and perceptions of these devices. A total of 15 Veterans diagnosed with a SMI participated in interviews. Both thematic analysis and rapid qualitative analysis approaches were used to uncover findings in key domains and emergent themes.ResultsWearable fitness trackers allowed participants to conveniently monitor various aspects of their physical and mental health, provided a greater understanding of their overall well-being, and motivated them to reach personal health goals. Individuals were open to sharing their personal health information collected from the devices with providers to improve their health care treatment and expressed no privacy concerns surrounding data tracking or the device’s global positioning system that monitors physical location. Participants experienced some technological challenges with using the fitness trackers, as well as the device’s accompanying cell phone app. Furthermore, participants expressed difficulties in understanding and interpreting the health data that was collected from the health and fitness trackers. Greater ongoing technological support, in addition to physical device adjustments to enhance comfort and usability, were suggested ways of improving overall user experience.ConclusionsParticipants with SMI in this sample were accepting of wearable mobile-monitoring devices and believe it is feasible to incorporate these fitness trackers into their daily lives. Furthermore, participants in this sample expressed no privacy concerns regarding location tracking or the sharing of health information collected from these devices with providers. Patient-generated health data collected from these devices may offer valuable information that could be used to inform health care treatment for this population.

Wearable health-tracking devices are being adopted by American self-insured companies to combat rising health insurance costs. The key motivation is to discourage employees’ unhealthy behavior through monitoring their data. While wearable health-tracking devices might improve users awareness about personal health, we argue that the introduction of such devices in organizational settings also risk introducing unforeseen challenges. In this paper we unpack the unforeseen challenges and argue that wearable health-tracking devices in organizational settings risk disciplining employees, by tempting or penalizing them financially. Further, health concerns are reduced to numbers through wearable health-tracking devices providing surveillance of bodies, impacting people’s lives. We stress how important it is that designers and researchers find ways to address these challenges in order to avoid future abuse of personal health data collected from wearable health-data tracking devices.

Occupational accidents cause physical harm to impacted employees and financial harm to employers. The most effective protection against occupational accidents is the proper usage of personal protective equipment (PPE). A huge variety of wearable sensors is available for different purposes, they have already proven their potential to support personal fitness and health. This paper describes the idea and implementation of a safety system for PPE based on such wearable sensors and wireless technology. The goal of the system is to ensure that the right PPE required for a specific task is worn. Furthermore, conclusions based on stakeholder interviews are presented. These include the feasibility of the suggested implementation on a smartwatch as well as a confirmation from domain experts that wearable sensing can improve workplace safety.

As the proportion of the world's elderly population continues to increase, wearable devices can provide ideas for solving a series of problems caused by population aging. Therefore, it is of great significance for the development of intelligent elderly care and the improvement of the quality of elderly care services to explore the factors that influence the intention of elderly users to accept wearable devices. An improved unified theory of acceptance and use of technology (UTAUT) model is constructed from the perspective of elderly individuals, and new parameters are added, including four factors related to wearable devices, including performance expectancy, perceived cost, hedonic value and aesthetic appeal, and three factors related to elderly individuals, including personal physiological conditions, health anxiety and personal innovativeness in information technology. The data analysis was accomplished with the partial least square regression structural equation modeling. The findings of this study revealed that performance expectancy, perceived cost, hedonic value and aesthetic appeal all have significant impact on elderly users' intention to use wearable devices. Furthermore, personal innovativeness in information technology, personal physiological condition, and intention to use all have significant impact on elderly users' actual usage behavior of wearable devices. However, there is no obvious relationship between health anxiety and actual usage behavior. Elderly adults' attention to wearable devices plays an important role in the development of the wearable device-related industry chain, which provides management suggestions for stakeholders.