Wearable Devices Research Articles

Self-Powered Machine-Learning-Assisted Material Identification Enabled by a Thermogalvanic Dual-Network Hydrogel with a High Thermopower.

Wearable devices equipped with high-performance flexible sensors that can identify diverse physical information free from batteries are playing an indispensable role in various fields. However, previous studies on flexible sensors have primarily focused on their elasticity and temperature-sensing capability, with few reports on material identification. In this paper, a thermogalvanic dual-network hydrogel is fabricated with [Fe(CN)6]3-/4- as a redox couple and lithium magnesium silicate, Gdm+ and lithium bromideas key electrolytes to optimize the interconnected porous structure of the gel, which shows excellent mechanical and thermoelectric properties with a thermopower as high as 4.01mV K-1. A self-powered material identification ring is developed based on the temperature-triggered thermoelectric response of the gel in conjunction with machine learning, which can actively infer materials without an external power connection by analyzing the voltage signals correlated with interfacial heat transfer produced upon contact with different materials. The proposed gel ring has important applications for future areas such as human-computer interaction and haptic-associated artificial intelligence.

PulseGuard: Health Monitoring and Analysis System

Abstract: The use of wearable devices such as smart watches has led to a major change in the way a person is able to manage their health. In this paper, health monitoring system construction based on a smartwatch is designed which is capable of Health parameters like heartbeat monitoring, counting steps and their physical activity levels at continuous intervals. The system is built on modern web technologies and cloud services for data processing in a real-time manner, thus ensuring that alert messages are delivered immediately in case health parameters go beyond the normal ranges. The goal is to improve personal health management by providing expansion, dependability and availability to the users in areas where there is poor or no health care provision. The paper discusses system architecture, implementation, and the prospect of the system in enhancing preventive medicine and its practice for better health.

IoT Based Sign to Speech Converter System

The idea introduces a system for facilitating communication between the deaf and the non-signing community through a wearable device called IoT based Sign-to-Speech Converter System. The System consist of glove & it utilizes Internet of Things (IoT) technology to bridge the communication gap by translating sign language (gestures) into spoken language in real-time. The core functionality of the system is augmented by seamless integration with an Android application, enhancing user experience and accessibility. The proposed system leverages a combination of sensors embedded within the glove to capture intricate hand movements and gestures commonly used in sign language. These data are transmitted wirelessly to a central processing unit, to interpret the gestures and generates corresponding spoken language output. The Android application serves as a user interface, allowing for customization of settings and additional features such as text-based communication. Effectiveness and practicality of the proposed system have been demonstrated, opening-up new avenues for inclusive communication and empowerment of the deaf community.

Unveiling Artificial Intelligence's Power: Precision, Personalization, and Progress in Rheumatology.

This review examines the increasing use of artificial intelligence (AI) in rheumatology, focusing on its potential impact in key areas. AI, including machine learning (ML) and deep learning (DL), is revolutionizing diagnosis, treatment personalization, and prognosis prediction in rheumatologic diseases. Specifically, AI models based on convolutional neural networks (CNNs) demonstrate significant efficacy in analyzing medical images for disease classification and severity assessment. Predictive AI models also have the ability to forecast disease trajectories and treatment responses, enabling more informed clinical decisions. The role of wearable devices and mobile applications in continuous disease monitoring is discussed, although their effectiveness varies across studies. Despite existing challenges, such as data privacy concerns and issues of model generalizability, the compelling results highlight the transformative potential of AI in rheumatologic disease management. As AI technologies continue to evolve, further research will be essential to address these challenges and fully harness the potential of AI to improve patient outcomes in rheumatology.

2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.

A novel digital health approach to improving global pediatric sepsis care in Bangladesh using wearable technology and machine learning.

Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient's admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson's correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models' detection of advanced sepsis and physicians' documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson's r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.

Analytic modeling and validation of strain in textile-based OLEDs for advanced textile display technologies

In the IoT era, the demand for wearable displays is rapidly growing, catalyzing the advancement of research into textile-based organic light-emitting diodes (OLEDs). This growing interest stems particularly from the inherent flexibility of textile-based OLEDs1,2, allowing for seamless integration into the dynamic and interactive functionalities of cutting-edge wearable technology, alongside their superior electrical performance. The durability and mechanical robustness of these displays, especially under physical stress and deformation, are critical to their practical application and longevity. Thus, understanding and enhancing the mechanical properties of textile-based OLEDs is paramount for their successful integration into wearable technologies. However, many studies assessing the mechanical properties of OLEDs have predominantly relied on simplistic bending test outcomes determined by the radius, often neglecting or insufficiently analyzing the strain exerted on the OLEDs atop textile substrates in relation to curvature of these devices. Existing analyses typically presume pure bending, though such an assumption leads to considerable errors in strain estimations, making such approaches problematic if the goal is practical application in actual wearable display products. To address these limitations, an analytic model that includes a comprehensive energy equation is introduced, considering the stretching energy, bending energy, and shear energy of each layer composing the textile substrate. This holistic approach provides a novel formula specifically designed to calculate the top surface strain of textile substrates. Robust validation of this formula is conducted by comparing its results with strain measurements obtained from digital image correlation (DIC) and finite element analysis (FEA) outcomes from ANSYS across various bending radii (or equivalently, curvatures). The close alignment of the calculated strain values with those derived from DIC and FEA not only underscores the precision of this formula but also highlights its significant potential for enhancing the designs and functionalities of future wearable display technologies under real-world conditions.

Advances in Cardiovascular Wearable Devices

Cardiovascular diseases are a leading cause of death worldwide. They mainly include coronary artery disease, rheumatic heart disease, andcerebrovascular disease, and. Cardiovascular diseases can be better managed and diagnosed using wearable devices. Wearable devices, in comparison to traditional cardiovascular diagnostic tools, are not only inexpensive but also have the potential to provide continuous real-time monitoring. This paper reviews some of the recent advances in cardiovascular wearable devices. It discusses traditional implantable devices for cardiovascular diseases as well as wearable devices. The different types of wearable devices are categorized based on different technologies, namely using galvanic contact, photoplethysmography (PPG), and radio frequency (RF) waves. It also highlights the use of artificial intelligence (AI) in cardiovascular disease diagnostics as well as future perspectives on cardiovascular devices.

A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios.

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system's reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts.

Sensors for Smoking Detection in Epidemiological Research: Scoping Review.

The use of wearable sensors is being explored as a challenging way to accurately identify smoking behaviors by measuring physiological and environmental factors in real-life settings. Although they hold potential benefits for aiding smoking cessation, no single wearable device currently achieves high accuracy in detecting smoking events. Furthermore, it is crucial to emphasize that this area of study is dynamic and requires ongoing updates. This scoping review aims to map the scientific literature for identifying the main sensors developed or used for tobacco smoke detection, with a specific focus on wearable sensors, as well as describe their key features and categorize them by type. According to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol, an electronic search was conducted on the PubMed, MEDLINE, and Web of Science databases, using the following keywords: ("biosensors" OR "biosensor" OR "sensors" OR "sensor" OR "wearable") AND ("smoking" OR "smoke"). Among a total of 37 studies included in this scoping review published between 2012 and March 2024, 16 described sensors based on wearable bands, 15 described multisensory systems, and 6 described other strategies to detect tobacco smoke exposure. Included studies provided details about the design or application of wearable sensors based on an elastic band to detect different aspects of tobacco smoke exposure (eg, arm, wrist, and finger movements, and lighting events). Some studies proposed a system composed of different sensor modalities (eg, Personal Automatic Cigarette Tracker [PACT], PACT 2.0, and AutoSense). Our scoping review has revealed both the obstacles and opportunities linked to wearable devices, offering valuable insights for future research initiatives. Tackling the recognized challenges and delving into potential avenues for enhancement could elevate wearable devices into even more effective tools for aiding smoking cessation. In this context, continuous research is essential to fine-tune and optimize these devices, guaranteeing their practicality and reliability in real-world applications.

The future of wearable health technology: From monitoring to preventive healthcare

Wearable health technology has undergone rapid development, evolving from simple fitness trackers to sophisticated medical devices that provide real-time monitoring and data collection. As these technologies become increasingly integrated into healthcare systems, their potential to shift from reactive treatment to proactive preventive care is gaining attention. This review paper explores the current state of wearable health devices, including the key technologies that enable monitoring and data analysis, and discusses the latest advancements such as AI integration, smart fabrics, and implantable wearables. The paper also delves into the significant role these technologies play in preventive healthcare by enabling continuous monitoring, early disease detection, and personalized health interventions. Furthermore, it addresses the challenges facing wearables, including issues of data accuracy, privacy, and user compliance. Finally, the paper explores future directions and ethical considerations, emphasizing the potential for wearables to reshape healthcare by promoting more accessible, equitable, and preventive healthcare solutions.

Personalized blood pressure estimation using multiview fusion information of wearable physiological signals and transfer learning

Continuous blood pressure (BP) monitoring is crucial for individual health management, yet the significant inter-individual variations among patients pose challenges to achieving precision medicine. In response to this issue, we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilities. Our proposed architecture exhibits superior performance in estimating systolic blood pressure and diastolic blood pressure, with average absolute errors of 3.94 mmHg and 2.24 mmHg, respectively. These results surpass existing baseline models and align with the standards set by the British Hypertension Society, the Association for the Advancement of Medical Instrumentation, and the Institute of Electrical and Electronics Engineers for BP measurement. Additionally, this study explores a personalized model fine-tuning strategy by adjusting specific layers and incorporating individual information, presenting an optimal solution. The model's generalization ability is validated through transfer learning across databases (public and self-made). To enhance the proposed architecture's usability in wearable devices, this study employs a knowledge distillation strategy for model lightweighting, with preliminary application in our designed real-time BP estimation system. This study provides an efficient and accurate solution for personalized BP estimation, exhibiting broad potential applications.

The intersection of clinical trial management and patient advocacy: How research professionals can promote patient rights while upholding clinical excellence

The purpose of this study was to investigate the intersection of clinical trial management and patient advocacy, focusing on how research professionals can promote patient rights while upholding clinical excellence. As clinical trials evolve to become more patient-centered, balancing ethical obligations with scientific rigor remains a significant challenge. This paper explores key concepts such as informed consent, patient engagement, data privacy, and the ethical responsibilities of research professionals. Additionally, it examines the role of emerging technologies, including mobile health applications, wearable devices, telemedicine, and blockchain, in enhancing patient advocacy and improving trial outcomes. A thorough literature review and analysis were conducted to identify the challenges faced in integrating patient advocacy into clinical trial management. The study also highlights the critical role of research professionals in ensuring that patient rights are respected while addressing the complexities of global clinical trials, where varying ethical standards pose additional challenges. The findings suggest that patient-centric approaches, supported by digital tools, can significantly improve the ethical integrity of clinical trials, ensuring greater inclusivity, accessibility, and retention of participants. However, these innovations also raise concerns regarding data security, necessitating the implementation of robust cybersecurity measures to protect patient confidentiality. In conclusion, the study recommends the adoption of decentralized clinical trials and the harmonization of global regulatory frameworks to ensure consistent ethical standards. Research professionals must prioritize patient welfare while embracing technological advancements that offer opportunities for more efficient and inclusive clinical research. By striking this balance, clinical trials can better serve both scientific progress and the ethical imperatives of patient advocacy.

A chip thermal management method realizing integrated applications of cooling, power generation and heat flow measurement based on thermoelectric effect

Thermoelectric cooling, power generation and heat flow measurement are widely used in frontier domains while each of them is still in an isolated state. They are now widely used in fields such as electronic cooling, wearable device, sensors and so on. This paper proposes a method of thermoelectric effect combined with pulse width modulation (PWM) to achieve integrating thermoelectric applications. In a PWM cycle, the integration of thermoelectric applications makes it possible to realize three different operations of temperature control, energy recovery and heat flow measurement through a single thermoelectric module. The experimental results indicate that increasing the input voltage of the thermoelectric module extends the duration for power generation and heat flow measurement, while also enhancing the energy recovery rate. When the target temperature is higher than the ambient temperature, although the temperature control accuracy slightly decreases with the increase of the target temperature, the average temperature fluctuation range of the whole experimental group is −0.09∼+0.1℃ and energy recovery rate increases by 79.9 %. When the target temperature is lower than the ambient temperature, the system can still achieve efficient energy recovery. Applying the method to the computer central processing unit(CPU), the experimental results present that the heat dissipation of CPU can be monitored by measuring heat flow. Compared with the CPU + FPU working, the total power generation is increased by 0.285 W and energy recovery rate is increased by 78.3 % when the CPU is idle.

Physical Engagement in Face-to-Face Interaction Is Associated with Depressive Symptoms of Interaction Partners in the Workplace

Individuals develop interdependence through interactions. The process of physical coordination during face-to-face interactions facilitates relationship formation, emotional experiences, and emotional contagion. Workplaces inherently involve organic and continuous face-to-face interactions. Examining the interpersonal association between physical movement and emotional state among employees in the workplace is crucial for understanding an organization’s emotional dynamics. This study investigated the association between patterns of physical engagement during face-to-face interactions and depressive symptoms with those of interaction partners in a real-world workplace setting. The analysis utilized face-to-face interaction data from 449 employees across ten organizations, measured using wearable devices, along with a self-reported depression scale. The results showed that employees’ average physical engagement negatively correlated with the average depressive symptoms of their interaction partners, rather than their own. The association remained robust regardless of the number of relationships or workplace type. Furthermore, the depressive symptoms of individuals who had a significant influence on interpersonal relationships within organizations negatively correlated with their and their partner’s physical engagement. Our findings have important implications for the epidemiological understanding of organizational mental health in relation to face-to-face interactions among employees.

Telehealth innovations for cardiovascular disease management

Telehealth innovations and wearable technologies are revolutionizing the management of cardiovascular diseases (CVD), providing patients with more accessible and continuous care. The integration of remote monitoring tools and mobile health applications allows for real-time tracking of vital signs, such as heart rate, blood pressure, and oxygen levels, enabling early detection of abnormalities and timely medical interventions. These technologies empower patients to actively participate in their own health management, encouraging adherence to prescribed lifestyle changes, such as increased physical activity and improved dietary habits, which are essential for preventing and controlling CVD. Wearable devices, such as smartwatches and fitness trackers, play a critical role in the continuous monitoring of cardiovascular health. Combined with telehealth platforms, they offer personalized insights and facilitate communication between patients and healthcare providers, enhancing care coordination. This leads to improved patient outcomes, particularly in managing chronic conditions like hypertension and heart failure, where early intervention is key to preventing complications. Additionally, telehealth solutions reduce the need for frequent in-person visits, cutting healthcare costs and making care more convenient and accessible for patients in remote or underserved areas. As telehealth and wearable technologies continue to advance, they hold the potential to further transform cardiovascular care, contributing to more efficient healthcare delivery and better management of cardiovascular risk factors.

Editorial: Precision Medicine Meets AI

There are rapid advancements in AI-driven diagnostics and wearable technologies, which are enabling early disease prediction, especially in conditions like Parkinson's disease. Precision medicine, underpinned by molecular biology and genomic data, allows clinicians to tailor treatment based on patient phenotypes, significantly enhancing personalized healthcare. AI complements this by processing vast datasets, from health records to biomarker evaluations, supporting more precise diagnostics and treatment recommendations.

A wearable and stretchable dual-wavelength LED device for home care of chronic infected wounds.

Phototherapy can offer a safe and non-invasive solution against infections, while promoting wound healing. Conventional phototherapeutic devices are bulky and limited to hospital use. To overcome these challenges, we developed a wearable, flexible red and blue LED (r&bLED) patch controlled by a mobile-connected system, enabling safe self-application at home. The patch exhibits excellent skin compatibility, flexibility, and comfort, with high safety under system supervision. Additionally, we synthesized a sprayable fibrin gel (F-gel) containing blue light-sensitive thymoquinone and red light-synergistic NADH. Combined with bLED, thymoquinone eradicated microbes and biofilms within minutes, regardless of antibiotic resistance. Furthermore, NADH and rLED synergistically improved macrophage and endothelial cell mitochondrial function, promoting wound healing, reducing inflammation, and enhancing angiogenesis, as validated in infected diabetic wounds in mice and minipigs. This innovative technology holds great promise for revolutionizing at-home phototherapy for chronic infected wounds.

Innovative Wearable Electronics: Next-Generation Nitrogen-Doped Lutetium-Carbon Microspheres Composites for Robust Energy Harvesting.

In the quest to advance wearable electronics, this study presents a novel method using nitrogen-doped lutetium-carbon microspheres (N, Lu-CMS) for high-performance piezoelectric energy harvesting. The synthesis of N, Lu-CMS begins with the polymerization of sucrose, followed by the preparation of N, Lu-CMS metal complexes through the incorporation of lutetium (III) nitrate hydrate and thiourea, yielding a black powder product. The wearable electronic device is designed with a silicon rubber (SR) matrix, reinforced with 0D fillers such as N, Lu-CMS, or molybdenum disulfide (MoS₂). Mechanical testing revealed a significant improvement in compressive modulus, reaching 3.7MPa (N, Lu-CMS) at a concentration of 3 parts per hundred rubber (phr). Electromechanical assessments demonstrated efficient energy conversion, while biomechanical analysis, including thumb pressing tests, showed a notable increase in output voltage, peaking at ≈285mV (N, Lu-CMS) at 3 phr. This research provides a foundation for future engineering applications, particularly in electronic packaging for wearable electronics and smart devices, underscoring the significant impact of N, Lu-CMS in this emerging field. The surface power density achieved is 0.026 nWcm- 2 (N, Lu-CMS) and 0.0056 nWcm- 2 (Hybrid). Lastly, the conversion efficiency is 6.26% for N, Lu-CMS, and 1.05% for the hybrid system.

Evaluating the Impact of Laundering on the Electrical Performance of Wearable Photovoltaic Cells: A Comparative Study of Current Consistency and Resistance

Wearable photovoltaic technology has been prominent in recent years because electronic devices need to be powered continuously without reliance on traditional methods. However, the practical adoption of wearable PV cells is hindered by the need for laundering, potentially degrading performance. This research compared PV cells’ maximum current and electrical resistance before and after laundering testing conditions. This study used eight samples of two types of PV panel cells and laundered them up to five cycles. The current and electrical resistance values were recorded before and after each laundering cycle. This study analyzed the data using a paired sample t-test and MANOVA. It was found that laundering cycles significantly affected the current values in both types of samples, with no differential impact between the types; on the other hand, laundering cycles did not significantly affect the electrical resistance values in both types of samples, with no differential impact between the types. These results are crucial for industries developing textile-based PV panels, where maintaining electrical performance after laundering is essential. These findings could pave the way for more sustainable, self-powered wearable PV technologies, ultimately transforming how users interact with electronic devices daily.