Search Results (78)

Integrating antibacterial fabrics into wearable technology represents a transformative advancement in healthcare, fashion, and personal hygiene. Antibacterial fabrics, designed to inhibit microbial growth, are gaining prominence due to their potential to reduce infections, enhance durability, and maintain cleanliness in wearable devices. These fabrics offer effective antimicrobial properties while retaining comfort and functionality by incorporating nanotechnology and advanced materials, such as silver nanoparticles, zinc oxide, titanium dioxide, and graphene. The production techniques for antibacterial textiles range from chemical and physical surface modifications to biological treatments, each tailored to achieve long-lasting antibacterial performance while preserving fabric comfort and breathability. Advanced methods such as nanoparticle embedding, sol–gel coating, electrospinning, and green synthesis approaches have shown significant promise in enhancing antibacterial efficacy and material compatibility. Wearable technology, including fitness trackers, smart clothing, and medical monitoring devices, relies on prolonged skin contact, making the prevention of bacterial colonization essential for user safety and product longevity. Antibacterial fabrics address these concerns by reducing odor, preventing skin irritation, and minimizing the risk of infection, especially in medical applications such as wound dressings and patient monitoring systems. Despite their potential, integrating antibacterial fabrics into wearable technology presents several challenges. This review provides a comprehensive overview of the key antibacterial agents, the production strategies used to fabricate antibacterial textiles, and their emerging applications in wearable technologies. It also highlights the need for interdisciplinary research to overcome current limitations and promote the development of sustainable, safe, and functional antibacterial fabrics for next-generation wearable. Full article

Background: Telemedicine can improve early symptom detection using medical devices and applications. It can also help identify barriers to patient adherence and enhance communication with healthcare professionals. This study aimed to evaluate the applicability, usability, and patient satisfaction with telemonitoring devices and apps for individuals with heart failure and type 2 diabetes. Methods: In a randomized study, patients in the Intervention Group received six months of nursing teleassistance and telemonitoring using a wearable electrocardiograph, a step tracker, and an App for recording clinical information and conducting video calls. Usability was measured using the System Usability Scale (SUS) and satisfaction with a six-item questionnaire. Results: A total of 43 patients (71 ± 8 years) were enrolled in the intervention group. A total of 41 (95%) of patients utilized the App daily, entering 13,048 information, 53 ± 59 per patient. The nurses performed 896 video-calls, 22 ± 21 per patient. The mean number of walking sessions recorded was 6.1 ± 0.9 per week (159 ± 24 per patient). Thirty-five patients (81%) used a 3-lead ECG and recorded 942 traces, 27 ± 14 per patient. At the end, 40 SUS were collected from patients: 15 (38%, 71 ± 7 years) considered the system excellent or good, 20 (50%, 71 ± 8 years) thought it fair, and 5 (13%, 74 ± 7 years) considered the system offered poor. The overall assessment of patient satisfaction with the service was 22 ± 3.3. Conclusions: This study provides evidence that, although technology can be complex for older adults, it is broadly accepted by most patients, especially when the benefits are understood. The support offered by nurses is essential for significantly enhancing the overall patient experience. Full article

Background: Maintaining physical activity (PA) is vital for older people, particularly those with frailty and mobility limitations. Wearable activity trackers and digital feedback tools show promise for encouraging PA, but their feasibility and acceptability in aged care remain underexplored. This study evaluated the feasibility and acceptability of using wearable and mobile devices for step tracking and examined the usability of three interfaces (Fitbit, mobile app, and website) for reviewing PA progress in aged care. Methods: This is a user experience and feasibility study that does not involve objective physical activity quantification or device performance analysis. It is a mixed-methods feasibility study conducted with 14 participants aged ≥65 years from residential and community aged care services in metropolitan and regional New South Wales, Australia. Participants used a Fitbit Inspire 3 linked to a study website and a mobile phone step-counting app to monitor their steps across the three interfaces for four weeks. Feasibility was evaluated through enrolment and retention, and acceptability through a facilitator-led survey. Quantitative items on usability, comfort, motivation and device preference were summarised descriptively; open-ended responses were analysed thematically to identify user experiences, benefits, and barriers. Results: Step tracking was feasible, with 82% enrolment and 93% retention. Participants preferred the Fitbit over the mobile phone or website due to its ease of use, visibility and more enjoyable experience. Step tracking increased awareness of PA and supported confidence to move more. Participants valued reminders, rewards and opportunities for social sharing. Reported barriers included illness, usability challenges and occasional technical issues. Conclusions: Wearable step trackers show promise for supporting PA among older people receiving aged care. Despite the small sample and short follow-up, strong acceptability signals suggest that simple digital tools could enhance the reach and sustainability of mobility-promoting interventions into aged care systems. Future studies should examine long-term adherence, usability across diverse mobility and cognitive needs, and conditions for successful scale-up. Full article

Wearable devices are becoming widely applied in healthcare to enable continuous, noninvasive monitoring, but their use in pediatric populations remains relatively underexplored. This review synthesizes 36 clinical studies focused on pediatric hospital and outpatient wearables published between 2014 and 2025. Devices included wrist-worn trackers, adhesive biosensors, and more, capturing electrocardiography, photoplethysmography, accelerometry, and other signals. Clinical applications spanned a variety of care settings. Artificial intelligence (AI) partially enhanced interpretation for the early detection of conditions such as postoperative complications and sepsis. Despite their promising accuracy, most studies remain small, single-center pilots focused on feasibility and signal validity rather than outcomes such as mortality, readmission, or long-term recovery. Key barriers include pediatric-specific device design, motion-robust signal quality, regulatory clearance, workflow integration, and equitable adoption in low-resource settings. Ethical concerns such as privacy, consent, and incidental findings and regulatory constraints, particularly the lack of pediatric labeling and approval for consumer and AI-driven devices, further limit translation into practice. Future work should prioritize multi-center studies, multimodal analytics, explainable AI, and seamless integration into clinical pathways. With these advances, wearables can move beyond feasibility to become reliable, personalized tools that improve pediatric monitoring and care. Full article

Health monitoring systems play a crucial role in every life. In the 21st century, advanced technologies like wearable sensors have emerged and make healthcare better overall. These sensors collect massive amounts of data about our health over time in many dimensions. In this paper, our objective is to develop and evaluate a machine learning-based clinical decision support system using wearable sensor data to accurately classify users’ physiological states and activity contexts. The most accurate and effective model is for identifying wearable sensor-based physiological signal classification. However, there are serious privacy and security issues with sending raw sensor data to centralized computers. We gathered the multivariate physiological and activity data from wearable technology, including smartwatches and fitness trackers, which make up the dataset. Physiological signals, including heart rate, resting heart rate, normalized heart rate, entropy of heart rate variability, and caloric expenditure, are all included in the dataset. Lying, sitting, self-paced walking, and running at different MET(Metabolic Equivalent of Task) levels are examples of activity context labels. To secure our data, we proposed an architecture based on federated learning that helps machine learning model training across several dispersed devices without exchanging raw data. In this study, we used eight classifiers, and these are XGBoost, RF, Extra Trees, LightGBM, CatBoost, Bagging, DT, and GB. It has been observed that XGBoost performs well in comparison to the other classifiers with an accuracy of 0.94, a precision of 0.90, a recall of 0.89, an F1-score of 0.90, and an AUC-ROC of 0.98. This study demonstrates the potential of wearable sensor data, combined with machine learning, for accurately classifying activity and physiological conditions. The ML boosting family, especially XGBoost, exhibited strong generalization across diverse signal inputs and activity contexts. These results suggest that explainable, non-invasive wearable analytics can support early detection and monitoring frameworks in personalized healthcare systems. The proposed federated learning framework effectively combines privacy-aware computation and accurate classification using wearable sensor data. Full article

Anterior cruciate ligament (ACL) injury and reconstruction (ACLR) are associated with biomechanical deficits and reinjury risk. Wearable devices offer promising tools for objective assessment of knee joint function. This scoping review aimed to map the use of wearable devices in quantifying knee outcomes following ACL injury or reconstruction, and to evaluate their clinical readiness and methodological quality. Eligible studies were human, English-language studies in ACL/ACLR populations or healthy cohorts assessing ACL-relevant knee outcomes with wearable devices. MEDLINE (Ovid), Embase (Ovid), APA PsycInfo (Ovid), PubMed, and Scopus were searched up to 27 August 2025. Data on devices, tasks, participants, outcomes, and validation were extracted, and an adapted technology readiness level (TRL) mapping was applied. Thirty-two studies met the inclusion criteria. Inertial measurement units (IMUs) were used most often for kinematics. Standalone accelerometers quantified pivot-shift features, while force-sensing insoles captured bilateral loading. Electromagnetic trackers and electrogoniometers served as higher-precision comparators but were workflow-limited. Reporting of calibration and criterion validation was inconsistent. TRL bands clustered at 3–6, and none reached clinical integration. We propose task-matched sampling, transparent calibration, criterion validation, pairing with patient-reported outcome measures (PROMs), and multi-site workflow trials to progress towards routine care. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. The accurate and continuous monitoring of these symptoms is essential for optimizing treatment strategies and improving patient outcomes. Traditionally, clinical assessments have relied on scales and methods that often lack the ability for continuous, real-time monitoring and can be subject to interpretation bias. Recent advancements in wearable technologies, such as smartphones, smartwatches, and activity trackers (ATs), present a promising alternative for more consistent and objective monitoring. This review aims to evaluate the use of smartphones and smart wrist devices, like smartwatches and activity trackers, in the management of PD, assessing their effectiveness in symptom evaluation and monitoring and physical performance improvement. Studies were identified by searching in PubMed, Scopus, Web of Science, and Cochrane Library. Only 13 studies of 1027 were included in our review. Smartphones, smartwatches, and activity trackers showed a growing potential in the assessment, monitoring, and improvement of motor symptoms in people with PD, compared to clinical scales and research-grade sensors. Their relatively low cost, accessibility, and usability support their integration into real-world clinical practice and exhibit validity to support PD management. Full article

This paper outlines a new system for processing of eye-tracking data in basketball live games with two pre-trained Artificial Intelligence (AI) models. blueThe system is designed to process and extract features from data of basketball coaches and referees, recorded with the Pupil Labs Neon Eye Tracker, a device that is specifically optimized for video analysis. The research aims to present a tool useful for understanding their visual attention patterns during the game, what they are attending to, for how long, and their physiological responses, blueas is evidenced through pupil size changes. AI models are used to monitor events and actions within the game and correlate these with eye-tracking data to provide understanding into referees’ and coaches’ cognitive processes and decision-making. This research contributes to the knowledge of sport psychology and performance analysis by introducing the potential of Artificial Intelligence (AI)-based eye-tracking analysis in sport with wearable technology and light neural networks that are capable of running in real time. Full article

The need for creative solutions in the real-time monitoring of health is rapidly increasing, especially in light of health incidents in relation to drivers of motor vehicles. A sensor-based health monitoring system provides an integrated mechanism for diagnosing and managing in real time, enabling the detection, prediction, and recommendation of treatment and the prevention of disease onset. The real-time monitoring of driver’s health represents a significant advancement in the assurance of driver safety and well-being. From fitness trackers to advanced biosensors, these devices have not only made healthcare more accessible but have also transformed how people interact with their health data. The purpose of this scoping review is to systematically collect and evaluate information from publications on driver health monitoring systems to provide a comprehensive overview of the current state of research on wearable or remote sensor technologies for driver health monitoring. It aims to identify knowledge gaps that need to be addressed and suggest future research directions that will help to fill these gaps. This approach involves the topic of vehicle safety and healthcare and will contribute to the advancement of this field. By focusing on the real-time monitoring of health parameters in an automotive context, this review highlights the potential of different types of technologies to bridge the gap between health monitoring and driver safety. Full article

Under the strategy of Healthy China, students’ physical health status not only affects their future life and studies but also influences social progress and development. By monitoring and measuring the daily PA levels of Chinese students over a week, this study aimed to fully understand the current PA status of students at different times, providing data support for improving students’ PA levels and physical health. (1) Wearable fitness trackers have advantages such as low cost, portable wearability, and intuitive test data. By exploring the differences between wearable devices and PA testing instruments, this study provides reference data to improve the accuracy of wearable devices and promote the use of fitness trackers instead of triaxial accelerometers, thereby advancing scientific research on PA and the development of mass fitness. A total of 261 students (147 males; 114 females) were randomly selected and wore both the Actigraph GT3X+ triaxial accelerometer and Huawei smart fitness trackers simultaneously to monitor their daily PA levels, energy metabolism, sedentary behavior, and step counts from the trackers over a week. The students’ PA status and living habits were also understood through literature reviews and questionnaire surveys. The validity of the smart fitness trackers was quantitatively analyzed using ActiLife software 6 Data Analysis Software and traditional analysis methods such as MedCal. Paired sample Wilcoxon signed-rank tests and mean absolute error ratio tests were used to assess the validity of the smart fitness trackers relative to the Actigraph GT3X+ triaxial accelerometer. A linear regression model was established to predict the step counts of the Actigraph GT3X+ triaxial accelerometer based on the step counts from the smart fitness trackers, aiming to improve the accuracy of human motion measurement by smart fitness trackers. There were significant differences in moderate-to-high-intensity PA time, energy expenditure, metabolic equivalents, and step counts between males and females (p < 0.01), with females having higher values than males in both moderate-to-high-intensity PA time and step counts. Sedentary behavior showed significant differences only on weekdays between males and females (p < 0.05), with females engaging in less sedentary behavior than males. (2) There was a significant difference in sedentary time between weekdays and weekends for students (p < 0.05), with sedentary time being higher on weekends than on weekdays. (3) Compared with weekends, female students had significantly different moderate-to-high-intensity PA time and sedentary time on weekdays (p < 0.01), while no significant differences were observed for male students. (4) Under free-living conditions, the average daily step count monitored by the smart fitness trackers was lower than that measured by the Actigraph GT3X+ triaxial accelerometer, with a significant difference (p < 0.01), but both showed a positive correlation (r = 0.727). (5) The linear regression equation established between the step counts monitored by the smart fitness trackers and those by the Actigraph GT3X+ triaxial accelerometer was y = 3677.3157 + 0.6069x. The equation’s R² = 0.625, with an F-test value of p < 0.001, indicating a high degree of fit between the step counts recorded by the Huawei fitness tracker and those recorded by the triaxial accelerometer. The t-test results for the regression coefficient and constant term were t = 26.4410 and p < 0.01, suggesting that both were meaningful. The tested students were able to meet the recommended total amount of moderate-intensity PA for 150 min per week or high-intensity PA for 75 min per week according to the “Chinese Adult PA Guidelines”, as well as the recommended daily step count of more than 6000 steps per day according to the “Chinese Dietary Guidelines”. (2) Female students had significantly more moderate-to-high-intensity PA time than male students, but lower energy expenditure and metabolic equivalents, which may have been related to their lifestyle and types of exercise. On weekends, female students significantly increased their moderate-to-high-intensity PA time compared with males but also showed increased sedentary time exceeding that of males; further investigation is needed to understand the reasons behind these findings. (3) The step counts monitored by the Huawei smart fitness trackers correlated with those measured by the Actigraph GT3X+ triaxial accelerometer, but the step counts from the fitness trackers were lower, indicating that the fitness trackers underestimated PA levels. (4) There was a linear relationship between the Huawei smart fitness trackers and the Actigraph GT3X+ triaxial accelerometer. By using the step counts monitored by the Huawei fitness trackers and the regression equation, it was possible to estimate the activity counts from the Actigraph GT3X+ triaxial accelerometer. Replacing the Actigraph GT3X+ triaxial accelerometer with Huawei smart fitness trackers for step count monitoring significantly reduces testing costs while providing consumers with intuitive data. Full article

Between 2018 and 2024, the global market has experienced significant advancements in sensor technologies for monitoring patients’ health conditions, which have demonstrated a pivotal role in diagnostics, treatment monitoring, and healthcare optimization. Progress in microelectronics, device miniaturization, and wireless communication technologies has facilitated the development of sophisticated sensors, including wearable devices such as smartwatches and fitness trackers, enabling the real-time monitoring of key health parameters. These devices are widely employed across clinical settings, nursing care, and daily life to collect critical data on vital signs, including heart rate, blood pressure, oxygen saturation, and respiratory rate. A systematic review of the developments within this period highlights the transformative potential of AI and IoT-based technologies in healthcare personalization, particularly in disease symptom prediction and public health management. Furthermore, innovative techniques such as respiratory inductive plethysmography (RIP) and millimeter-wave radar systems (mmTAA) have emerged as precise, non-contact solutions for respiratory monitoring, with applications spanning diagnostics, therapeutic interventions, and enhanced safety in daily life. Full article

In recent years, the field of wearable sensors has undergone significant evolution, emerging as a pivotal topic of research due to the capacity of such sensors to gather physiological data during various human activities. Transitioning from basic fitness trackers, these sensors are continuously being improved, with the ultimate objective to make compact, sophisticated, highly integrated, and adaptable multi-functional devices that seamlessly connect to clothing or the body, and continuously monitor bodily signals without impeding the wearer’s comfort or well-being. Potentiometric sensors, leveraging a range of different solid contact materials, have emerged as a preferred choice for wearable chemical or biological sensors. Nanomaterials play a pivotal role, offering unique properties, such as high conductivity and surface-to-volume ratios. This article provides a review of recent advancements in wearable potentiometric sensors utilizing various solid contacts, with a particular emphasis on nanomaterials. These sensors are employed for precise ion concentration determinations, notably sodium, potassium, calcium, magnesium, ammonium, and chloride, in human biological fluids. This review highlights two primary applications, that is, (1) the enhancement of athletic performance by continuous monitoring of ion levels in sweat to gauge the athlete’s health status, and (2) the facilitation of clinical diagnosis and preventive healthcare by monitoring the health status of patients, in particular to detect early signs of dehydration, fatigue, and muscle spasms. Full article

The global healthcare market is expanding, with a particular focus on personalized care for individuals who are unable to leave their homes due to the COVID-19 pandemic. However, the implementation of personalized care is challenging due to the need for additional devices, such as smartwatches and wearable trackers. This study aims to develop a human body simulation that predicts and visualizes an individual’s 3D body changes based on 2D images taken by a portable device. The simulation proposed in this study uses semantic segmentation and image-based reconstruction techniques to preprocess 2D images and construct 3D body models. It also considers the user’s exercise plan to enable the visualization of 3D body changes. The proposed simulation was developed based on human-in-the-loop experimental results and literature data. The experiment shows that there is no statistical difference between the simulated body and actual anthropometric measurement with a p-value of 0.3483 in the paired t-test. The proposed simulation provides an accurate and efficient estimation of the human body in a 3D environment, without the need for expensive equipment such as a 3D scanner or scanning uniform, unlike the existing anthropometry approach. This can promote preventive treatment for individuals who lack access to healthcare. Full article

Background: This study aimed to gather data on physical activity (PA), bleeding, health-related quality of life, and health status, using a wearable device and an electronic patient-reported outcome (ePRO) app, in individuals with moderate or severe hemophilia A (HA) without inhibitors receiving treatment according to the clinical practice. Methods: This is a 12-month multicenter cohort study conducted in Italy. The primary outcomes included the description of PA by type and intensity, adherence to World Health Organization guidelines, bleeding, and health-related quality of life by EQ-5D questionnaire. PA data were collected continuously through a fitness tracker worn by the patient; all the other variables were collected through ePRO questionnaires. Results: Only 54 of the 103 enrolled subjects (52.4%) used their fitness tracker for the defined valid period; adolescents were the least compliant age group. PA was performed at low rates and intensity. Approximately 52% of the subjects had sedentary behavior. The mean EQ-5D values did not change over time. At least one bleeding was reported in 43.7% of the subjects, mostly with sedentary behavior. The PA in the 2 days preceding the bleeding was comparable to the one observed in the overall observational period. Conclusions: The systematic recording of data through a fitness tracker and ePRO app shows that subjects with HA without inhibitors have lower-than-expected PA and that they still experience issues related to bleeding. Full article

Background: The field of smart devices and physical activity is evolving rapidly, with a wide range of devices measuring a wide range of parameters. Scientific articles look at very different populations in terms of the impact of smart devices but do not take into account which characteristics of the devices are important for the group and which may influence the effectiveness of the device. In our study, we aimed to analyse articles about the impact of smart devices on physical activity and identify the characteristics of different target groups. Methods: Queries were run on two major databases (PubMed and Web of Science) between 2017 and 2024. Duplicates were filtered out, and according to a few main criteria, inappropriate studies were excluded so that 37 relevant articles were included in a more detailed analysis. Results: Four main target groups were identified: healthy individuals, people with chronic diseases, elderly people, and competitive athletes. We identified the essential attributes of smart devices by target groups. For the elderly, an easy-to-use application is needed. In the case of women, children, and elderly people, gamification can be used well, but for athletes, specific measurement tools and accuracy may have paramount importance. For most groups, regular text messages or notifications are important. Conclusions: The use of smart devices can have a positive impact on physical activity, but the context and target group must be taken into account to achieve effectiveness. Full article