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Purpose: This study explores the initial views of people with age-related macular degeneration towards wearable electronic vision enhancement systems. Methods: Ten adults with age-related macular degeneration participated in semi-structured interviews, which were analysed using reflexive thematic analysis. Results: Four themes were identified. Firstly, participants spoke of the wide-ranging impact of sight loss and how current helpful coping strategies still had significant limitations, affecting their desire to seek new solutions. The second theme showed that "other people" offered welcomed support with existing electronic coping solutions and are needed to provide suitable advice and training. However, "other people" limited the acceptability of using new solutions in public places. The third theme captured participants' desire for a wearable aid providing image magnification and enhancement over a range of distances. The final theme covered the reality of some current wearable technology, perceived as heavy, enclosing, or strange in appearance. Appearance caused some to lose interest in use, although others reframed the devices' desired usefulness to solo and sedentary activities. Conclusion: This population are interested in the potential benefits of wearable electronic vision enhancement systems. More work is needed to understand the suitability of current solutions due to participant concerns about training, appearance and performance. IMPLICATIONS FOR REHABILITATION: A device that offers image enhancement and variable magnification in a hands-free, wearable form is very desirable to people with age-related macular degeneration. Some potential users are not seeking new solutions to well-described problems, which may be a useful coping strategy but alternatively may be motivated by fear of the unknown, financial worries, or concerns about appropriate training. The weight and appearance of some of the current wearable electronic vision enhancement systems are not immediately appealing and would stop some from proceeding with a performance trial. After viewing the current devices, the desirable times to use a wearable electronic vision enhancement system may be reframed by users to focus predominately on sedentary tasks taking place in isolation at home. [ABSTRACT FROM AUTHOR]

Graphene has emerged as one of the most promising materials for next‐generation gas sensor platforms due to its high flexibility, transparency, and hydrophobicity. However, graphene shows inherent low selectivity in gas sensing. This has led to extensive development of noble‐metal decoration on graphene to modulate its surface chemistry for enhanced selectivity. While noble metals such as Pt, Pd, and Au have widely been employed to functionalize graphene surface, non‐noble metal decoration of graphene has remained underexplored. Here, an unprecedented room‐temperature self‐activated graphene gas sensor functionalized by NiO nanoparticles and its application to wearable devices monitoring ammonia gas in daily life are demonstrated. NiO‐functionalized graphene micropatterns show ultra‐high selectivity to ammonia with a low detection limit of 2.547 ppt. Density functional theory (DFT) calculations reveal that the strong attraction between NiO and NH3 induced by charge depletion and the vertex region of NiO accelerate the adsorption of NH3 molecules. Furthermore, a wearable graphene device demonstrates the capability to detect ammonia emissions from beef, triggering an alarm call when a specific threshold is exceeded. This work proposes the functionalization of graphene with transition metal oxides, extending beyond the conventional noble metal decoration, and the potential utilization of the graphene for wearable devices. [ABSTRACT FROM AUTHOR]

Human body temperature is a critical physiological indicator, reflecting metabolism and temperature regulation. Biomimetic temperature sensors with high temporal and spatial resolution are highly desired to emulate human skin's temperature perception capabilities. Here, a selective filtration method is proposed to fabricate a jointless p‐n thermoelectric module, integrating p‐type and n‐type materials with minimal interfacial barriers. The structure enables rapid response, minimal signal variation, and a robust linear relationship between open circuit voltage and temperature difference, making it suitable as a biomimetic thermoreceptor for enhancing humanoid robots with intelligent sensing capabilities. A smart thermoelectric sensing system with a high temporal and spatial resolution is developed using with jointless p‐n module as the sensing element. In temporal thermal sensing, the sensing system can timely and effectively identify the heat source, with a fast response time of <0.1 s. In addition, in spatial thermal sensing, the system reliably detects thermal stimuli within discrete regions of each measuring area of <1 × 1 cm2. This study provides an effective strategy for temperature sensors in thermoelectric sensing and biomimetic thermoreceptor applications. [ABSTRACT FROM AUTHOR]

With the wide application of portable electronic devices, zinc–ion hybrid supercapacitors (ZIHCs) have aroused great interest. However, balancing the low‐temperature performance and high energy density of ZIHCs remains a huge challenge. Herein, a zwitterionic hydrogel electrolyte (ZHE) loaded with carboxymethylcellulose and MXene is designed for ZIHCs, which shows excellent frost resistance and high output voltage. Carboxymethylcellulose and MXene enhance the flexibility and conductivity of the zwitterionic hydrogel electrolyte. Moreover, When ZnCl2 is introduced into a gel electrolyte, it induces an increase in the rate of ion transport, which enables a broadening of the operating temperature of the hydrogel (−40 °C–25 °C). As a result, Zn//AC ZIHCs based on ZHE show a high voltage window of 2 V, a high energy density (specific capacity) of 137 Wh kg−1 (247 F g−1), and the potential for wearable devices. This study will provide an effective strategy for the design of hydrogel ZIHCs with wide voltage windows, high energy density, and high practicality. [ABSTRACT FROM AUTHOR]

Nearly all geriatric surgical complications are studied in the context of a single organ system, e.g., cardiac complications and the heart; delirium and the brain; infections and the immune system. Yet, we know that advanced age, physiological stress, and infection all increase sympathetic and decrease parasympathetic nervous system function. Parasympathetic function is mediated through the vagus nerve, which connects the heart, brain, and immune system to form, what we have termed, the brain–heart-immune axis. We hypothesize that this brain–heart-immune axis plays a critical role in surgical recovery among older adults. In particular, we hypothesize that the brain–heart-immune axis plays a critical role in the most common surgical complication among older adults: postoperative delirium. Further, we present heart rate variability as a measure that may eventually become a multi-system vital sign evaluating brain–heart-immune axis function. Finally, we suggest the brain–heart-immune axis as a potential interventional target for bio-electronic neuro-immune modulation to enhance resilient surgical recovery among older adults. [ABSTRACT FROM AUTHOR]

Low-cost, portable devices capable of accurate physiological measurements are attractive tools for coaches, athletes, and practitioners. The purpose of this study was primarily to establish the validity and reliability of Movesense HR+ ECG measurements compared to the criterion three-lead ECG, and secondarily, to test the industry leader Garmin HRM. Twenty-one healthy adults participated in running and cycling incremental test protocols to exhaustion, both with rest before and after. Movesense HR+ demonstrated consistent and accurate R-peak detection, with an overall sensitivity of 99.7% and precision of 99.6% compared to the criterion; Garmin HRM sensitivity and precision were 84.7% and 87.7%, respectively. Bland–Altman analysis compared to the criterion indicated mean differences (SD) in RR' intervals of 0.23 (22.3) ms for Movesense HR+ at rest and 0.38 (18.7) ms during the incremental test. The mean difference for Garmin HRM-Pro at rest was −8.5 (111.5) ms and 27.7 (128.7) ms for the incremental test. The incremental test correlation was very strong (r = 0.98) between Movesense HR+ and criterion, and moderate (r = 0.66) for Garmin HRM-Pro. This study developed a robust peak detection algorithm and data collection protocol for Movesense HR+ and established its validity and reliability for ECG measurement. [ABSTRACT FROM AUTHOR]

Assessing the olfactory preferences of consumers is an important aspect of fragrance product development and marketing. With the advancement of wearable device technologies, physiological signals hold great potential for evaluating olfactory preferences. However, there is currently a lack of relevant studies and specific explanatory procedures for preference assessment methods that are based on physiological signals. In response to this gap, a synchronous data acquisition system was established using the ErgoLAB multi-channel physiology instrument and olfactory experience tester. Thirty-three participants were recruited for the olfactory preference experiments, and three types of autonomic response data (skin conductance, respiration, and heart rate) were collected. The results of both individual and overall analyses indicated that olfactory preferences can lead to changes in skin conductance (SC), respiration (RESP), and heart rate (HR). The trends of change in both RESP and HR showed significant differences (with the HR being more easily distinguishable), while the SC did not exhibit significant differences across different olfactory perception preferences. Additionally, gender differences did not result in significant variations. Therefore, HR is more suitable for evaluating olfactory perception preferences, followed by RESP, while SC shows the least effect. Moreover, a logistic regression model with a high accuracy (84.1%) in predicting olfactory perception preferences was developed using the changes in the RESP and HR features. This study has significant implications for advancing the assessment of consumer olfactory preferences. [ABSTRACT FROM AUTHOR]

In many regions globally, including low-resource settings, there is a growing trend towards using mHealth technology, such as wearable sensors, to enhance health behaviors and outcomes. However, adoption of such devices in research conducted in low-resource settings lags behind use in high-resource areas. Moreover, there is a scarcity of research that specifically examines the user experience, readiness for and challenges of integrating wearable sensors into health research and community interventions in low-resource settings specifically. This study summarizes the reactions and experiences of young women (N = 57), ages 18 to 24 years, living in poverty in Kampala, Uganda, who wore Garmin vívoactive 3 smartwatches for five days for a research project. Data collected from the Garmins included participant location, sleep, and heart rate. Through six focus group discussions, we gathered insights about the participants' experiences and perceptions of the wearable devices. Overall, the wearable devices were met with great interest and enthusiasm by participants. The findings were organized across 10 domains to highlight reactions and experiences pertaining to device settings, challenges encountered with the device, reports of discomfort/comfort, satisfaction, changes in daily activities, changes to sleep, speculative device usage, community reactions, community dynamics and curiosity, and general device comfort. The study sheds light on the introduction of new technology in a low-resource setting and also on the complex interplay between technology and culture in Kampala's slums. We also learned some insights into how wearable devices and perceptions may influence behaviors and social dynamics. These practical insights are shared to benefit future research and applications by health practitioners and clinicians to advance and enhance the implementation and effectiveness of wearable devices in similar contexts and populations. These insights and user experiences, if incorporated, may enhance device acceptance and data quality for those conducting research in similar settings or seeking to address population-specific needs and health issues. [ABSTRACT FROM AUTHOR]

Wearable technologies using inertial sensors are an alternative for gait assessment. However, their psychometric properties in evaluating post-stroke patients are still being determined. This systematic review aimed to evaluate the psychometric properties of wearable technologies used to assess post-stroke gait and analyze their reliability and measurement error. The review also investigated which wearable technologies have been used to assess angular changes in post-stroke gait. The present review included studies in English with no publication date restrictions that evaluated the psychometric properties (e.g., validity, reliability, responsiveness, and measurement error) of wearable technologies used to assess post-stroke gait. Searches were conducted from February to March 2023 in the following databases: Cochrane Central Registry of Controlled Trials (CENTRAL), Medline/PubMed, EMBASE Ovid, CINAHL EBSCO, PsycINFO Ovid, IEEE Xplore Digital Library (IEEE), and Physiotherapy Evidence Database (PEDro); the gray literature was also verified. The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) risk-of-bias tool was used to assess the quality of the studies that analyzed reliability and measurement error. Forty-two studies investigating validity (37 studies), reliability (16 studies), and measurement error (6 studies) of wearable technologies were included. Devices presented good reliability in measuring gait speed and step count; however, the quality of the evidence supporting this was low. The evidence of measurement error in step counts was indeterminate. Moreover, only two studies obtained angular results using wearable technology. Wearable technologies have demonstrated reliability in analyzing gait parameters (gait speed and step count) among post-stroke patients. However, higher-quality studies should be conducted to improve the quality of evidence and to address the measurement error assessment. Also, few studies used wearable technology to analyze angular changes during post-stroke gait. • Validity, reliability and measurement error of the devices have been investigated. • Devices presented good reliability in measuring gait parameters post-stroke. • Evidence on reliability is sufficient but of low quality. • Evidence on measurement error is sufficient but of low quality. • Few studies use portable devices to analyze angular changes. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to evaluate the accuracy of peripheral oxygen saturation (SpO2) measurements from Polar Elixir™ pulse oximetry technology compared with arterial oxygen saturation (SaO2) measurements during acute stepwise steady-state inspired hypoxia at rest. A post hoc objective was to determine if SpO2 measurements could be improved by recalibrating the Polar Elixir™ algorithm with SaO2 values from a random subset of participants. Methods: The International Organization for Standardization (ISO) protocol (ISO 80601-2-61:2017) for evaluating the SpO2 accuracy of pulse oximeter equipment was followed whereby five plateaus of SaO2 between 70% and 100%were achieved using stepwise reductions in inspired O2 during supine rest. Blood samples drawn through a radial arterial catheter from 25 participants were first used to compare SaO2 with SpO2 measurements from Polar Elixir™. Then the Polar Elixir™ algorithm was recalibrated using SaO2 data from 13 random participants, and SpO2 estimates were recalculated for the other 12 participants. For SaO2 values between 70%and 100%, root mean square error, intraclass correlation coefficients (ICC), Pearson correlations, and Bland--Altman plots were used to assess the accuracy, agreement, and strength of relationship between SaO2 values and SpO2 values from Polar Elixir™. Results: The initial root mean square error for Polar Elixir™ was 4.13%. After recalibrating the algorithm, the RMSE was improved to 2.67%. The ICC revealed excellent levels of agreement between SaO2 and Polar Elixir™SpO2 values both before (ICC(1,3) = 0.837, df = 574, P < 0.001) and after (ICC(1,3) = 0.942, df = 287, P < 0.001) recalibration. Conclusions: Relative to ISO standards, Polar Elixir™ yielded accurate SpO2 measurements during stepwise inspired hypoxia at restwhen comparedwith SaO2 values, which were improved by recalibrating the algorithm using a subset of the SaO2 data. [ABSTRACT FROM AUTHOR]

• There is a growing implementation of device-based population surveillance of physical activity and sedentary behavior in the Nordic countries. • So far, none of the countries have collected device-based data on pre-schoolers or older adults in a national sample. • Steps per day and mean level of moderate-to-high intensity physical activity are similar across the countries and above the current recommendations for health benefits. • The amount of sedentary time is high in all Nordic countries. • Type of device, placement, and data procedures vary both within and between the Nordic countries, and more standardized data collection is needed for better comparisons. The purpose of this scoping review was to summarize and describe the methodology and results from population-based studies of physical activity and sedentary time measured with devices in the Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) and published in 2000 or later. A systematic search was carried out in PubMed and Web of Science in June 2023 using predefined search terms. Fourteen unique research projects or surveillance studies were identified. Additionally, 2 surveillance studies published by national agencies were included, resulting in a total of 16 studies for inclusion. National surveillance systems exist in Finland and Norway, with regular survey waves in school-aged children/adolescents and adults. In Denmark, recent nationally representative data have been collected in school children only. So far, Sweden has no regular national surveillance system using device-based data collection. No studies were found from Iceland. The first study was conducted in 2001 and the most recent in 2022, with most data collected from 2016 to date. Five studies included children/adolescents 6–18 years, no study included preschoolers. In total 11 studies included adults, of which 8 also covered older adults. No study focused specifically on older adults. The analytical sample size ranged from 205 to 27,890. Detailed methodology is presented, such as information on sampling strategy, device type and placement, wear protocols, and physical activity classification schemes. Levels of physical activity and sedentary time in children/adolescents, adults, and older adults across the Nordic countries are presented. A growing implementation of device-based population surveillance of physical activity and sedentary behavior in the Nordic countries has been identified. The variety of devices, placement, and data procedures both within and between the Nordic countries highlights the challenges when it comes to comparing study outcomes as well as the need for more standardized data collection. [Display omitted] [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted that effective early infection detection methods are essential, as they play a critical role in controlling the epidemic spread. In this work, we investigate the use of wearable sensors in conjunction with machine learning (ML) techniques for pandemic infection detection. We work on designing a wristband that measures various vital parameters such as temperature, heart rate, and SPO2, and transmits them to a mobile application using Bluetooth Low Energy. The accuracy of the wristband measurements is shown to be within 10% of the readings of existing commercial products. The measured data can be used and analyzed for various purposes. To benefit from the existing online datasets related to COVID-19, we use this pandemic as an example in our work. Hence, we also develop ML-based models that use the measured vital parameters along with cough sounds in order to determine whether a case is COVID-19 positive or not. The proposed models are shown to achieve remarkable results, exceeding 90% accuracy. One of our proposed models exceeds 96% performance in terms of accuracy, precision, recall, and F1-Score. The system lends itself reasonably for amendment to deal with future pandemics by considering their specific features and designing the ML models accordingly. Furthermore, we design and develop a mobile application that shows the data collected from the wristband, records cough sounds, runs the ML model, and provides feedback to the user about their health status in a user-friendly, intuitive manner. A successful deployment of such an approach would decrease the load on hospitals and prevent infection from overcrowded spaces inside the hospital. [ABSTRACT FROM AUTHOR]

Droplet transmission is the primary infection route for respiratory diseases like COVID‐19 and influenza, but small and low‐cost wearable droplet detection devices are a significant challenge. Herein, a respiratory droplet micro‐sensor based on graphene oxide quantum dots (GOQDs) assembled onto SiO2 microspheres by the nebulized natural deposition is presented. Benefiting from the energy dissipation of the microsphere to droplets, the sensor can detect droplets as far as 2 m from coughing. With this sensor, droplet signal variations caused by some factors like distance, speech, angles, and wind directions are explored, and the effectiveness of different protective measures in preventing droplet transmission is evaluated. This droplet detection technology is expected to be utilized for the development of personal detection and protection devices against infectious respiratory diseases. [ABSTRACT FROM AUTHOR]

Sleep plays a role in maintaining our physical well-being. However, sleep-related issues impact millions of people globally. Accurate monitoring of sleep is vital for identifying and addressing these problems. While traditional methods like polysomnography (PSG) are commonly used in settings, they may not fully capture natural sleep patterns at home. Moreover, PSG equipment can disrupt sleep quality. In recent years, there has been growing interest in the use of sensors for sleep monitoring. These lightweight sensors can be easily integrated into textiles or wearable devices using technology. The flexible sensors can be designed for skin contact to offer continuous monitoring without being obtrusive in a home environment. This review presents an overview of the advancements made in flexible sensors for tracking body movements during sleep, which focus on their principles, mechanisms, and strategies for improved flexibility, practical applications, and future trends. [ABSTRACT FROM AUTHOR]

Schematic illustration of plantar pressure distribution visualization system including a 9-channel pressure sensor array, analog to digital converter, data analysis software, and operation interface. [Display omitted] Based on real-time detection of plantar pressure, gait recognition could provide important health information for rehabilitation administration, fatigue prevention, and sports training assessment. So far, such researches are extremely limited due to lacking of reliable, stable and comfortable plantar pressure sensors. Herein, a strategy for preparing high compression strength and resilience conductive iongels has been proposed by implanting physically entangled polymer chains with covalently cross-linked networks. The resulting iongels have excellent mechanical properties including nice compliance (young's modulus < 300 kPa), high compression strength (>10 MPa at a strain of 90 %), and good resilience (self-recovery within seconds). And capacitive pressure sensor composed by them possesses excellent sensitivity, good linear response even under very small stress (∼kPa), and long-term durability (cycles > 100,000) under high-stress conditions (133 kPa). Then, capacitive pressure sensor arrays have been prepared for high-precision detection of plantar pressure spatial distribution, which also exhibit excellent sensing performances and long-term stability. Further, an extremely sensitive and fast response plantar pressure monitoring system has been designed for monitoring plantar pressure of foot at different postures including upright, forward and backward. The system achieves real-time tracking and monitoring of changes of plantar pressure during different static and dynamic posture processes. And the characteristics of plantar pressure information can be digitally and photography displayed. Finally, we propose an intelligent framework for real-time detection of plantar pressure by combining electronic insoles with data analysis system, which presents excellent applications in sport trainings and safety precautions. [ABSTRACT FROM AUTHOR]

Patient-reported outcomes (PROs) are essential for understanding the effects of MS and its treatments on patients’ lives; they play an important role in multiple sclerosis (MS) research and practice. We present the protocol for an observational study to prospectively assess the effect of cladribine tablets on PROs and their correlation to disability and physical activity in adults with highly active relapsing MS switching from a first disease modifying drug (DMD) to cladribine tablets in routine clinical practice at study sites in Italy. The primary objective will be to evaluate changes from baseline in the impact of highly active MS on self-assessed physical functioning 52  weeks after the switch to cladribine tablets using the Multiple Sclerosis Impact Scale-29 (MSIS-29). Secondary objectives will include self-assessed psychological impact of highly active MS in daily life and general health after the switch to cladribine tablets as well as changes in cognitive function, anxiety, and depression symptoms. Additional PRO measures will include the Hospital Anxiety and Depression Scale (HADS), the EuroQoL 5-Dimension 5-Level (EQ-5D-5L), the Work Productivity and Activity Impairment Questionnaire: Multiple Sclerosis (WPAI:MS), and the Patient-Reported Outcomes Measurement Information System (PROMIS). Wearable devices will acquire activity data (step counts, walking speed, time asleep, and energy expenditure). Additional clinical, radiological, and laboratory data will be collected when available during routine management. The findings will complement data from controlled trials by providing insight from daily clinical practice into the effect of cladribine tablets on the patient’s experience and self-assessed impact of treatment on daily life. [ABSTRACT FROM AUTHOR]

Wearable devices especially smartwatches are widely used in our daily life. With their increased use, a large amount of sensitive data are collected, stored, and managed in those devices. To protect sensitive data, encryption is often used but, traditional encryption is vulnerable to a novel coercive attack in which the adversary can capture the device's user and coerce the user to disclose the decryption key. To defend against the coercive attack, Plausibly Deniable Encryption (PDE) has been designed which can allow the victim user to deny the existence of hidden sensitive data. The PDE systems have been explored broadly for smartphones. However, the PDE systems which are suitable for wearable devices are still missing in the literature. In this work, we have designed MobiWear, the first PDE system specifically designed for wearable devices. By leveraging PDE, image steganography as well as watermarking, MobiWear ensures plausible deniability and can be easily deployed at the application layer. In addition, MobiWear relies on sensors equipped with the wearable devices to enter passwords, accommodating the wearable devices which have small-size screens and are inconvenient for entering plaintext. Security analysis and experimental evaluation using a real-world prototype (ported to an LG G smartwatch) show that MobiWear can ensure the deniability with a small computational overhead as well as a tiny degradation of the perceived quality of the image. [ABSTRACT FROM AUTHOR]

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios. [ABSTRACT FROM AUTHOR]

In the context in which severe visual impairment significantly affects human life, this article emphasizes the potential of Artificial Intelligence (AI) and Visible Light Communications (VLC) in developing future assistive technologies. Toward this path, the article summarizes the features of some commercial assistance solutions, and debates the characteristics of VLC and AI, emphasizing their compatibility with blind individuals' needs. Additionally, this work highlights the AI potential in the efficient early detection of eye diseases. This article also reviews the existing work oriented toward VLC integration in blind persons' assistive applications, showing the existing progress and emphasizing the high potential associated with VLC use. In the end, this work provides a roadmap toward the development of an integrated AI-based VLC assistance solution for visually impaired people, pointing out the high potential and some of the steps to follow. As far as we know, this is the first comprehensive work which focuses on the integration of AI and VLC technologies in visually impaired persons' assistance domain. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology. [ABSTRACT FROM AUTHOR]

Motor neuron disease (MND) is a rapidly progressive condition traditionally assessed using a questionnaire to evaluate physical function, the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R). Its use can be associated with poor sensitivity in detecting subtle changes over time and there is an urgent need for more sensitive and specific outcome measures. The ActiGraph GT9X is a wearable device containing multiple sensors that can be used to provide metrics that represent physical activity. The primary aim of this study was to investigate the initial suitability and acceptability of limb-worn wearable devices to group of people with MND in Scotland. A secondary aim was to explore the preliminary associations between the accelerometer sensor data within the ActiGraph GT9X and established measures of physical function. 10 participants with MND completed a 12-week schedule of assessments including fortnightly study visits, both in-person and over videoconferencing software. Participants wore the device on their right wrist and right ankle for a series of movements, during a 6-min walking test and for a period of 24-h wear, including overnight. Participants also completed an ALSFRS-R and questionnaires on their experience with the devices. 80% of the participants found wearing these devices to be a positive experience and no one reported interference with daily living or added burden. However, 30% of the participants experienced technical issues with their devices. Data from the wearable devices correlated with established measures of physical function. [ABSTRACT FROM AUTHOR]

Objective: This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving. Background: Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems. Method: Thirty participants performed three simulated automated driving tasks. After each task, the Situation Awareness Global Assessment Technique (SAGAT) was deployed to capture their SA about key elements that could affect their takeover task performance. Participants eye movements and brain activities were recorded. Data on their brain activity after each eye fixation on the key elements were extracted and labeled according to the correctness of the SAGAT. Mixed-effects models were used to identify brain regions that were indicative of SA, and machine learning models for SA assessment were developed based on the identified brain regions. Results: Participants' alpha and theta oscillation at frontal and temporal areas are indicative of SA. In addition, the FRBP technique can be used to predict drivers' SA with an accuracy of 88% using a neural network model. Conclusion: The FRBP technique, which incorporates eye movements and brain activities, can provide more comprehensive evaluation of SA. Findings highlight the potential of utilizing FRBP to monitor drivers' SA in real-time. Application: The proposed framework can be expanded and applied to driver state monitoring systems to measure human SA in real-world driving. [ABSTRACT FROM AUTHOR]

The increasing prevalence of wearable devices has sparked a growing interest in real‐time health monitoring and physiological parameter tracking. This study focuses on the development of a cost‐effective sweat analysis device, utilizing microfluidic technology and selective electrochemical electrodes for non‐invasive monitoring of glucose and potassium ions. The device, through real‐time monitoring of glucose and potassium ion levels in sweat during physical activity, issues a warning signal when reaching experimentally set thresholds (K+ concentration at 7.5 mM, glucose concentrations at 60 μM and 120 μM). This alerts users to potential dehydration and hypoglycemic conditions. Through the integration of microfluidic devices and precise electrochemical analysis techniques, the device enables accurate and real‐time monitoring of glucose and potassium ions in sweat. This advancement in wearable technology holds significant potential for personalized health management and preventive care, promoting overall well‐being, and optimizing performance during physical activities. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) monitoring has recently been an important indicator of cardiac health diagnosis. In the past, ECG could be measured under limited conditions in hospitals with 12‐lead electrode systems. Recently, portable and wearable devices have offered continuous, real‐time monitoring of ECG signals in real life. However, developing wearable ECG sensors that provide low‐motion artifacts and high‐quality signals during exercise conditions is still challenging. Herein, this review reports a systematic summary of the key characteristics, properties, and requirements of flexible wearable ECG devices for the early diagnosis of heart dysfunction in dynamic motions, including exercise. In addition, the recent progress in controlling sensor adhesion and novel materials for designing dry electrodes are discussed to improve ECG signal quality in exercise. Finally, various aspects of electrode developmental challenges and limitations are reviewed, and research directions for future studies are discussed. [ABSTRACT FROM AUTHOR]

Sleep disturbances are common after total knee arthroplasty (TKA). Despite the rising popularity of wearables to track sleep, little evidence exists in the arthroplasty literature regarding their efficacy. We aimed to correlate validated wearable sleep metrics with patient-reported sleep quality following TKA. Patients undergoing primary TKA were consecutively enrolled. Patients used a wearable device preoperatively and 90 days postoperatively to track five previously-validated measures of sleep. Each month, they rated their sleep quality. Wearable sleep data was correlated with patient-reported sleep quality using a point biserial correlation test. Categorical data were compared using Chi -square tests. A total of 110 patients were included. Preoperatively, 20.8% of patients reported "fairly bad" or "very bad" sleep; this increased to 44.4% 30 days postoperatively, then decreased to 26.5% 60 days postoperatively, and to 20.2% 90 days postoperatively. At 30 days postoperatively, time in bed, time asleep, and minutes of rapid eye movement sleep weakly correlated with patient-reported sleep quality (correlations 0.356, 0.345, and 0.345, respectively; P <.001). Sleep quality did not correlate with any wearable metric collected 60 or 90 days postoperatively. Patient-reported sleep quality following TKA initially worsened postoperatively, then improved to preoperative levels by 90 days. Time in bed, time asleep, and rapid eye movement sleep minutes only weakly correlated with patient-reported sleep quality at 30 days; no other correlations were detected. Surgeons that utilize remote monitoring following TKA should be aware that surrogate measures generated from these devices may correlate weakly, if at all, with the patient-reported outcome of the parameter being studied. [ABSTRACT FROM AUTHOR]

Interest in wearable technology and the need for eco-friendly solutions have spurred new methodologies. This research examines how sophisticated deep learning and biomimetic designs benefit each other. The results may change smart technology forever. The introduction highlights the global appeal of wearable technology and the importance of environmental considerations in design. Deep learning and biomimicry are a fresh and exciting combination that might increase smart device accuracy, energy efficiency, and biomimicry. This project seamlessly integrates biomimetic design elements with deep learning methods. Biomimicry affects wearable technology design and functioning. However, deep learning techniques based on artificial neural networks boost user flexibility and predictive analytics. The controlled experiment allows a thorough examination of a number of datasets designed to cover a wide range of biomimetic settings and user behaviours. The data prove that the proposed technique beats alternatives across several performance parameters. Integrating biomimetic principles with deep learning systems boosts accuracy. This proves the system's reliability. The biomimetic method is eco-friendly since energy efficiency grows dramatically. Biological mimicry indications show that the suggested strategy resembles natural systems. A new exploratory method enhances sustainable technologies. Integrating biomimicry and deep learning efficiently enhances gadget performance and meets environmental standards. This research emphasizes the transformational power of nature-friendly technology, changing our worldview. Our study helps ensure that upcoming wearable technologies are cutting-edge and ecologically beneficial. Deep learning and biomimetic designs are converging, marking a tipping point in sustainable technology. This helps move toward an eco-friendly future. [ABSTRACT FROM AUTHOR]

Gait-type recognition is important for robotic exoskeletons and walking-assistance devices to adjust their output according to the users' needs. However, the growing trend of using machine learning (ML) models is both labor- and data-intensive, which makes it practically less attractive for application in exoskeletons and wearable-assistive devices. This research aims to devise a fuzzy-based gait recognition algorithm that requires minimum training data (only 40 cycles for each of the 5 gait types) and adapts to new users without having the need of pre-training for each of them. The proposed algorithm uses the fuzzy logic system (FLS) and Welford's (variance computation) method to enhance the adaptability by adjusting the rules for gait-type recognition and fine-tuning them in real time for every new user without requiring a specific prior training. Simulation-based evaluation of the proposed algorithm shows a gait-type recognition accuracy of 63.0%, an improvement of 36.8% over the non-adaptive fuzzy-based recognition algorithm. Moreover, the results show that the proposed algorithm outperforms the popular ML methods (support vector machine, Naive Bayes classifier, and logistic regression) when subjected to limited gait-cycles data and no prior training is provided. [ABSTRACT FROM AUTHOR]

The growth in the prevalence of dementias is associated with a phenomenon that challenges the 21st century, population aging. Dementias require physical and mental effort on the part of caregivers, making it difficult to promote controlled and active care. This review aims to explore the usability and integration of wearable devices designed to measure the daily activities of elderly people with dementia. A survey was carried out in the following databases: LILACS, Science Direct and PubMed, between 2018 and 2024 and the methodologies as well as the selection criteria are briefly described. A total of 27 articles were included in the review that met the inclusion criteria and answered the research question. As the main conclusions, the various monitoring measurements and interaction aspects are critically important, demonstrating their significant contributions to controlled, adequate and active monitoring, despite the incomplete compliance with the key aspects which could guarantee solutions economically accessible to institutions or other organizations through the application of the design requirements. Future research should not only focus on the development wearable devices that follow the essential requirements but also on further studying the needs and adversities that elderly people with dementia face as a pillar for the development of a feasible device. [ABSTRACT FROM AUTHOR]

Thermo‐electrochemical cells (TECs) provide a new potential for self‐powered devices by converting heat energy into electricity. However, challenges still remain in the fabrication of flexible and tough gel electrolytes and their compatibility with redox actives; otherwise, contact problems exist between electrolytes and electrodes during stretching or twisting. Here, a novel robust and neutral hydrogel with outstanding stretchability was developed via double‐network of crosslinked carboxymethyl chitosan and polyacrylamide, which accommodated both n‐type (Fe2+/Fe3+) and p‐type ([Fe(CN)6]3−/[Fe(CN)6]4−) redox couples and maintained stretchability (>300%) and recoverability (95% compression). Moreover, poly(3,4‐ethylenedioxythiophene):poly(styrene sulfonate) textile electrodes with porous structure are integrated into gel electrolytes that avoid contact issues and effectively boost the Pmax of n‐ and p‐type thermocell by 76% and 26%, respectively. The optimized thermocell exhibits a quick current density response and is continually fully operational under deformations, which satisfies the working conditions of wearable devices. Multiple thermocells (four pairs) are effectively connected in alternating single n‐ and p‐type cells in series and outputted nearly 74.3 mV at ΔT = 10°C. The wearable device is manufactured into a soft‐pack thermocells to successfully harvest human body heat and illuminate an LED, demonstrating the potential of the actual application of the thermocell devices. [ABSTRACT FROM AUTHOR]

Cerebral palsy is a disorder of central motor and postural development, resulting in limited mobility. Cerebral palsy is often accompanied by cognitive impairment and abnormal behavior, significantly impacting individuals and society. Time, energy, and economic investment in the rehabilitation process is substantial, yet the rehabilitation outcomes often remain unsatisfactory. Additionally, some patients have limited sensory perception during rehabilitation training, making it challenging to effectively regulate exercise intensity. Traditional evaluation methods are mostly based on recovery performance, lack guidance at the neurophysiological level, and have an unequal distribution of medical rehabilitation resources, which pose great challenges to the rehabilitation of patients. Based on the issues mentioned above, this paper proposes a real-time cerebral signal monitoring system based on wearable devices. This system can monitor and store blood oxygen, heart rate, myoelectric, and EEG signals during cerebral palsy rehabilitation, and it can track and monitor signals during the rehabilitation treatment process. The system includes two parts: hardware design and software design. The hardware design includes a data signal acquisition module, a main control chip (ESP32), a muscle electrical sensor module, a brain electrical sensor module, a blood/heart rate acquisition module, etc. It is primarily for real-time signal data acquisition, processing, and uploading to the cloud server. The software design includes functions such as data receiving, data processing, data storage, network configuration, and remote communication and enables the visual monitoring of data signals. The system can achieve real-time monitoring of electromyography, electroencephalography, and blood oxygen levels, as well as the heart rate of patients with cerebral palsy, and adjust rehabilitation training in real-time during the rehabilitation process. At the same time, based on the real-time storage of the original electromyography and electroencephalography data, it can provide auxiliary guidance for later rehabilitation evaluation and effective data support for the entire rehabilitation treatment process. [ABSTRACT FROM AUTHOR]

The emergence of cutting‐edge cross‐disciplines has motivated the rapid development of wearable technology and flexible electronics. The flexibility and tunable properties of organic materials enable organic flexible electronics to adapt to complex surface deformations and achieve sensitive detection of physiological signals. The cost‐effectiveness of organic materials in mass production offers additional possibilities for the practical and commercialization of e‐skin technology. However, how to ensure stability and long‐term reliability while maintaining a highly sensitive, flexible, and stretchable is a challenge for e‐skins. In this review, the research progress and development trend of e‐skin is systematically summarized, especially the latest breakthroughs and innovations in the frontier of organic flexible electronics, and systematically review the applications of e‐skin in sensors, physiological monitoring, and energy supply. In addition, the review further discusses the prospects and current challenges for the application of organic flexible electronics in e‐skin, which provides a one‐stop reference for the development of e‐skin. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) is a highly promising research area meant to automatically identify and interpret human behaviour using data received from sensors in various contexts. The potential uses of HAR are many, among them health care, sports coaching or monitoring the elderly or disabled. Nonetheless, there are numerous hurdles to be circumvented for HAR's precision and usefulness to be improved. One of the challenges is that there is no uniformity in data collection and annotation making it difficult to compare findings among different studies. Furthermore, more comprehensive datasets are necessary so as to include a wider range of human activities in different contexts while complex activities, which consist of multiple sub‐activities, are still a challenge for recognition systems. Researchers have proposed new frontiers such as multi‐modal sensor data fusion and deep learning approaches for enhancing HAR accuracy while addressing these issues. Also, we are seeing more non‐traditional applications such as robotics and virtual reality/augmented world going forward with their use cases of HAR. This article offers an extensive review on the recent advances in HAR and highlights the major challenges facing this field as well as future opportunities for further researches. [ABSTRACT FROM AUTHOR]

Yarn‐woven triboelectric nanogenerators (TENGs) have greatly advanced wearable sensor technology, but their limited sensitivity and stability hinder broad adoption. To address these limitations, Poly(VDF‐TrFE) and P(olyadiohexylenediamine (PA66)‐based nanofibers coaxial yarns (NCYs) combining coaxial conjugated electrospinning and online conductive adhesive coating are developed. The integration of these NCYs led to enhanced TENGs (NCY‐TENGs), notable for their flexibility, stretchability, and improved sensitivity, which is ideal for capturing body motion signals. One significant application of this technology is the fabrication of smart insoles from NCY‐TENG plain‐woven fabrics. These insoles are highly sensitive and possess antibacterial, breathable, and washable properties, making them ideal for real‐time gait monitoring in patients with diabetic foot conditions. The NCY‐TENGs and their derivatives show immense potential for a variety of wearable electronic devices, representing a considerable advancement in the field of wearable sensors. [ABSTRACT FROM AUTHOR]

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC's implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants' homes. Our results demonstrate HEROIC's capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions. [ABSTRACT FROM AUTHOR]

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit sensors installed as wearable devices. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based and distance-based strategies, are employed to achieve the desired classification performance. The proposed system is shown to be highly accurate and computationally efficient for different algorithmic parameters, which is relevant in the context of limited computing resources on typical wearable devices. Extensive experimentation and simulation study have been conducted on multi-user human activity data from the public domain in order to analyze the trade-off between classification accuracy and computation complexity of the proposed learning paradigm with different algorithmic hyper-parameters. With 4.17 h of training time for 8000 activity episodes, the proposed SSL approach consumes at most 20 KB of CPU memory space, while providing a maximum accuracy of 90% and 100% classification rates. [ABSTRACT FROM AUTHOR]

A stretchable conductive Janus hydrogel with controllable porous structures is developed for highly sensitive monitoring of human motion. The Janus porous structure of the hydrogel is fabricated using patterned single‐layer droplets produced by microfluidics as pore‐forming templates. The hydrogel possess dual‐crosslinked polymeric networks to achieve good stretchability (480%), which allows fully cover the stretching range of human skin (usually <200%). Meanwhile, the hydrogel possess controllable Janus porous structures with local strain/stress enhancement to achieve high sensitivity (GF = 5.2) for strain‐sensing. The Janus hydrogel shows fast response for stretching (175 ms) and recovery (170 ms), and good durability (200 cycles at 50% strain). The conductive Janus hydrogel with high sensitivity and good mechanical properties are promising for developing soft wearable sensors in fields such as human health monitoring and human motion detection. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusions\nIMPLICATIONS FOR REHABILITATIONWearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end products meet clinical needs and can be adopted for patient care.In this cross-sectional mixed-methods study, an anonymous online survey was used to gather clinicians’ perceptions of a wearable robotic hand orthosis for post-stroke hemiparesis. Participants were asked about their clinical experience and provided feedback on the prototype device after viewing a video.154 participants completed the survey. Only 18.8% had previous experience with robotic technology. The majority of participants (64.9%) reported that they would use the device for both rehabilitative and assistive purposes. Participants perceived that the device could be used in supervised clinical settings with all phases of stroke. Participants also indicated a need for insurance coverage and quick setup time.Engaging clinicians early in the design process can help guide the development of wearable robotic devices. Both rehabilitative and assistive functions are valued by clinicians and should be considered during device development. Future research is needed to understand a broader set of stakeholders’ perspectives on utility and design.Clinicians valued both assistive and rehabilitative uses of a wearable robotic hand orthosis designed for individuals with hemiparesis after stroke.Wearable robotic hand devices should have the capacity to engage in functional, real-world activities for both assistive and rehabilitative purposes.Pragmatic factors, such as set-up and training time, must be balanced with device complexity to enable implementation in clinical settings.Stakeholders, such as clinicians, play an important role in identifying design priorities for wearable robotic devices to ensure these devices can meet the needs of end-users.Clinicians valued both assistive and rehabilitative uses of a wearable robotic hand orthosis designed for individuals with hemiparesis after stroke.Wearable robotic hand devices should have the capacity to engage in functional, real-world activities for both assistive and rehabilitative purposes.Pragmatic factors, such as set-up and training time, must be balanced with device complexity to enable implementation in clinical settings.Stakeholders, such as clinicians, play an important role in identifying design priorities for wearable robotic devices to ensure these devices can meet the needs of end-users. [ABSTRACT FROM AUTHOR]

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users' stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study. [ABSTRACT FROM AUTHOR]

The vestibular system is dedicated to gaze stabilization, postural balance, and spatial orientation; this makes vestibular function crucial for our ability to interact effectively with our environment. Vestibular hypofunction (VH) progresses over time, and it presents differently in its early and advanced stages. In the initial stages of VH, the effects of VH are mitigated using vestibular rehabilitation therapy (VRT), which can be facilitated with the aid of technology. At more advanced stages of VH, novel techniques that use wearable technologies for sensory augmentation and sensory substitution have been applied to manage VH. Despite this, the potential of assistive technologies for VH management remains underexplored over the past decades. Hence, in this review article, we present the state-of-the-art technologies for facilitating early-stage VRT and for managing advanced-stage VH. Also, challenges and strategies on how these technologies can be improved to enable long-term ambulatory and home use are presented. [ABSTRACT FROM AUTHOR]

Simple Summary: This research explores how wearable devices can be used to monitor patients undergoing colorectal surgery. By reviewing existing studies, we found that wearable devices are used in various stages of surgery. Devices worn primarily before surgery focus on assessing patient physical status and preparation, while devices worn primarily after surgery are used to monitor surgical complications. The findings from 20 studies and 10 different wearable devices suggest that standardised use of these devices could lead to more proactive and personalised care, ultimately improving surgical outcomes and patient well-being. Wearable devices are increasingly utilised to monitor patients perioperatively, allowing for continuous data collection and early complication detection. There is considerable variability in the types and usage settings of wearables, particularly within colorectal surgery. To address this, a scoping review was conducted to investigate current utilisation of wearable devices in colorectal surgery. A systematic search across MEDLINE and Embase was conducted following PRISMA Scoping Review guidelines. Results were synthesised narratively, categorised by perioperative phase (preoperative; postoperative; combination), and supplemented with descriptive statistics and tables. Out of 1525 studies initially identified, 20 were included, reporting data on 10 different wearable devices. Use of wearable devices varied across settings with those used preoperatively tending to focus on baseline physical status or prehabilitation, while postoperative use centred around monitoring and identification of complications. Wearable devices can enhance perioperative monitoring, enable proactive interventions, and promote personalised care for improved patient outcomes in colorectal surgery. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to develop a machine learning algorithm using an off‐the‐shelf digital watch, the Samsung watch (SM‐R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. Methods: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty‐eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave‐one‐patient‐out cross‐validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed‐and‐frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video‐electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. Results: LOPO CV results showed balanced accuracy of.93 (95% confidence interval [CI] =.8–.98), precision of.68 (95% CI =.46–.85), sensitivity of.87 (95% CI =.62–.96), and FAR of.21/24 h (interquartile range [IQR] = 0–.90). Testing the algorithm on patients without seizure resulted in an FAR of.28/24 h (IQR = 0–.61). During the "fixed‐and‐frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of.25/24 h (IQR = 0–.89). Feature importance showed that heart rate‐based features outperformed accelerometer/gyroscope‐based features. Significance: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom‐built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non‐EEG‐based seizure surveillance and forecasting in the clinical setting. [ABSTRACT FROM AUTHOR]

Advancements in wearable electronics and Internet of Things (IoT) sensors have catalyzed the need for effective micro‐energy harvesting. Piezoelectric nanogenerators (PENGs) are ideal due to their high conversion efficiency and durability. However, the contrast between the high piezoelectric coefficients of brittle inorganic ceramics and the lower coefficients of superior flexibility and biocompatibility of organic polymers poses a significant challenge. This work introduces a novel multilayer composite PENG, integrating a single‐crystal BaTiO3 (BTO) film between poly(vinylidene fluoride‐co‐trifluoroethylene) (PVDF‐TrFE) layers. The PVDF‐TrFE/BTO/PVDF‐TrFE PENGs demonstrate substantially improved energy harvesting performance, with outputs reaching up to 15.1 V, 2.39 µA, and power density of 17.33 µW cm−2 during bending deformation. This power density represents a significant increase compared to pure PVDF‐TrFE and nanoparticle BTO‐doped PVDF‐TrFE PENGs. Durability tests show consistent performance, with a stable ∼15.0 V output across 2000 bending cycles. Additionally, when attached to the human body, these PENGs efficiently convert body motions into electrical responses. This work demonstrates a significant enhancement in the performance of PENGs using sandwich‐structured composite of PVDF‐TrFE and freestanding single‐crystal BTO films, showing its potential to address the power requirements of wearable and flexible electronic devices. [ABSTRACT FROM AUTHOR]

Background: Quantitative 3D movement analysis using inertial measurement units (IMUs) allows for a more detailed characterization of motor patterns than clinical assessment alone. It is essential to discriminate between gait features that are responsive or unresponsive to current therapies to better understand the underlying pathophysiological basis and identify potential therapeutic strategies. Objectives: This study aims to characterize the responsiveness and temporal evolution of different gait subcomponents in Parkinson's disease (PD) patients in their OFF and various ON states following levodopa administration, utilizing both wearable sensors and the gold‐standard MDS‐UPDRS motor part III. Methods: Seventeen PD patients were assessed while wearing a full‐body set of 15 IMUs in their OFF state and at 20‐minute intervals following the administration of a supra‐threshold levodopa dose. Gait was reconstructed using a biomechanical model of the human body to quantify how each feature was modulated. Comparisons with non‐PD control subjects were conducted in parallel. Results: Significant motor changes were observed in both the upper and lower limbs according to the MDS‐UPDRS III, 40 minutes after levodopa intake. IMU‐assisted 3D kinematics detected significant motor alterations as early as 20 minutes after levodopa administration, particularly in upper limbs metrics. Although all "pace‐domain" gait features showed significant improvement in the Best‐ON state, most rhythmicity, asymmetry, and variability features did not. Conclusion: IMUs are capable of detecting motor alterations earlier and in a more comprehensive manner than the MDS‐UPDRS III. The upper limbs respond more rapidly to levodopa, possibly reflecting distinct thresholds to levodopa across striatal regions. [ABSTRACT FROM AUTHOR]

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Background: Sleep is disturbed in Rett syndrome (RTT), a rare and progressive neurodevelopmental disorder primarily affecting female patients (prevalence 7.1/100,000 female patients) linked to pathogenic variations in the X-linked methyl-CpG-binding protein 2 (MECP2) gene. Autonomic nervous system dysfunction with a predominance of the sympathetic nervous system (SNS) over the parasympathetic nervous system (PSNS) is reported in RTT, along with exercise fatigue and increased sudden death risk. The aim of the present study was to test the feasibility of a continuous 24 h non-invasive home monitoring of the biological vitals (biovitals) by an innovative wearable sensor device in pediatric and adolescent/adult RTT patients. Methods: A total of 10 female patients (mean age 18.3 ± 9.4 years, range 4.7–35.5 years) with typical RTT and MECP2 pathogenic variations were enrolled. Clinical severity was assessed by validated scales. Heart rate (HR), respiratory rate (RR), and skin temperature (SkT) were monitored by the YouCare Wearable Medical Device (Accyourate Group SpA, L’Aquila, Italy). The average percentage of maximum HR (HRmax%) was calculated. Heart rate variability (HRV) was expressed by consolidated time-domain and frequency-domain parameters. The HR/LF (low frequency) ratio, indicating SNS activation under dynamic exercise, was calculated. Simultaneous continuous measurement of indoor air quality variables was performed and the patients’ contributions to the surrounding water vapor partial pressure [PH2O (pt)] and carbon dioxide [PCO2 (pt)] were indirectly estimated. Results: Of the 6,559.79 h of biovital recordings, 5051.03 h (77%) were valid for data interpretation. Sleep and wake hours were 9.0 ± 1.1 h and 14.9 ± 1.1 h, respectively. HRmax % [median: 71.86% (interquartile range 61.03–82%)] and HR/LF [median: 3.75 (interquartile range 3.19–5.05)] were elevated, independent from the wake–sleep cycle. The majority of HRV time- and frequency-domain parameters were significantly higher in the pediatric patients (p ≤ 0.031). The HRV HR/LF ratio was associated with phenotype severity, disease progression, clinical sleep disorder, subclinical hypoxia, and electroencephalographic observations of multifocal epileptic activity and general background slowing. Conclusion: Our findings indicate the feasibility of a continuous 24-h non-invasive home monitoring of biovital parameters in RTT. Moreover, for the first time, HRmax% and the HR/LF ratio were identified as potential objective markers of fatigue, illness severity, and disease progression. [ABSTRACT FROM AUTHOR]

Introduction: Prolonged cardiac monitoring after cryptogenic stroke or embolic stroke of undetermined source (ESUS) is necessary to identify atrial fibrillation (AF) that requires anticoagulation. Wearable devices may improve AF detection compared to conventional management. We aimed to review the evidence for the use of wearable devices in post-cryptogenic stroke and post-ESUS monitoring. Methods: We performed a systematic search of PubMed, EMBASE, Scopus and clinicaltrials.gov on 21 July 2022, identifying all studies that investigated the use of wearable devices in patients with cryptogenic stroke or ESUS. The outcomes of AF detection were analysed. Literature reports on electrocardiogram (ECG)-based (external wearable, handheld, patch, mobile cardiac telemetry [MCT], smartwatch) and photoplethysmography (PPG)-based (smartwatch, smartphone) devices were summarised. Results: A total of 27 relevant studies were included (two randomised controlled trials, seven prospective trials, 10 cohort studies, six case series and two case reports). Only four studies compared wearable technology to Holter monitoring or implantable loop recorder, and these studies showed no significant differences on meta-analysis (odds ratio 2.35, 95% confidence interval [CI] 0.74-7.48, I² = 70%). External wearable devices detected AF in 20.7% (95% CI 14.9-27.2, I² = 76%) of patients and MCT detected new AF in 9.6% (95% CI 7.4%-11.9%, I² = 56%) of patients. Other devices investigated included patch sensors, handheld ECG recorders and PPG-based smartphone apps, which demonstrated feasibility in the post-cryptogenic stroke and post-ESUS setting. Conclusion: Wearable devices that are ECG or PPG based are effective for paroxysmal AF detection after cryptogenic stroke and ESUS, but further studies are needed to establish how they compare with Holter monitors and implantable loop recorder. [ABSTRACT FROM AUTHOR]

Utilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during the 2019–2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the covered distances and sprinting variables, while the second component focused on HR measures and training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize distance and intensity variables, with the ACC and DEC being particularly influential, whereas the second and subsequent components included HR measures and perceived exertion. On the three training days analyzed, the first component primarily consisted of variables related to the distance covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and decelerations. The high intensity demands have a high relative weight throughout the standard microcycle, which means that the training load needs to be carefully monitored and managed. [ABSTRACT FROM AUTHOR]

The development of wearable electronics is revolutionizing human health monitoring, intelligent robotics, and informatics. Yet the reliance on traditional batteries limits their wearability, user comfort, and continuous use. Energy harvesting technologies offer a promising power solution by converting ambient energy from the human body or surrounding environment into electrical power. Despite their potential, current studies often focus on individual modules under specific conditions, which limits practical applicability in diverse real-world environments. Here, this review highlights the recent progress, potential, and technological challenges in energy harvesting technology and accompanying technologies to construct a practical powering module, including power management and energy storage devices for wearable device developments. Also, this paper offers perspectives on designing next-generation wearable soft electronics that enhance quality of life and foster broader adoption in various aspects of daily life. [ABSTRACT FROM AUTHOR]