Search

AbstractOlder adults have become potential users of smart wearable devices. However, existing wearable devices mainly target young people. To clarify the needs of older users and highlight ways to improve applications, services, and experiences for this target demographic, we conducted an exploratory study. We recruited 39 adults aged 50 years and older for participatory research with semi-structured interviews examining their lifestyles, preferences for various wearable device features and interaction styles, and expectations for future devices. The results indicate that older adults expect wearable devices to be seamlessly integrated into their daily lives, providing accurate monitoring data and further assisting in enhancing physical abilities. Additionally, wearable devices for older adults should be designed according to the context of use, with products providing appropriate activities, guidance, and practical recommendations. These research findings provide knowledge that designers can apply to future improved wearable devices that enhance older adults’ user experiences and quality of life. [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]

Wearable technologies are anticipated to flourish in the fourth industrial revolution, enabling employers to integrate data into organizational systems. This study examined whether readiness for wearables can predict employees’ acceptance of these devices in the workplace. It explored the factors of technology readiness influencing acceptance and attitudes towards wearable use. A quantitative research approach was adopted, involving 8,081 employees from the South African Department of Employment and Labour (DEL). Census sampling was employed, with questionnaires distributed electronically, resulting in 351 responses. The Technology Readiness and Acceptance Model (TRAM) was the measuring instrument. Four hypotheses were formulated to investigate the relationship between readiness and acceptance of wearables. Results indicated that eagerness, optimism, and discomfort in the technology readiness scales significantly predicted technology acceptance. Specifically, optimism and eagerness were consistent positive predictors, while discomfort negatively impacted two of the three acceptance dimensions. Insecurity was not found to be a predictor of acceptance. The study recommends revising DEL's digital transformation policy to embrace wearables, emphasizing their potential for seamless integration. Additionally, it identifies a positive attitude as a new dimension of the TRAM, contributing to establishing a Wearable Readiness and Acceptance Model (WRAM), which has not been previously introduced in the literature. [ABSTRACT FROM AUTHOR]

Respiratory diseases are currently monitored through traditional pulmonary function tests, such as spirometry. However, the restrictions of these procedures, particularly in the context of the COVID‐19 pandemic, have underscored the need for alternative approaches to respiratory health assessment. Wearable devices have emerged as a promising solution, providing continuous data collection, and overcoming the limitations posed by conventional methods. This review explores the multifaceted field of wearable devices for respiratory monitoring, presenting the most common sensing technologies applied to pulmonary ventilation, their constituent materials, fabrication techniques, and diverse morphologies to enhance sensor performance. The role of machine learning algorithms and ethical data sharing is highlighted, contributing to the forthcoming patient‐centered healthcare landscape. Ultimately, the importance of validation and calibration protocols for wearable devices is underlined. In anticipation of evolving healthcare needs, this in‐depth study addresses the current challenges in wearable respiratory monitoring while laying a robust foundation for a personalized, connected, and ethically sound future for respiratory care. [ABSTRACT FROM AUTHOR]

This study developed a wearable mixed reality (MR)-based mobile learning system and applied it to museum learning activities to explore whether the proposed system can effectively improve university students' learning outcomes, situational interest, and cognitive load. Four museums were selected as the informal learning sites, and 64 university students were randomly divided into the experimental group (using the wearable MR-based mobile learning system) and the control group (using the conventional mobile learning system). The results show that in the retention and comprehension dimensions of the learning outcome, the experimental group has significantly better learning performance than the control group. The experimental group showed improvements in almost all dimensions of situational interest, except for the challenge dimension, while their extraneous cognitive load was reduced. Furthermore, this study found that the proposed system has a positive influence on museum learning in higher education, and has good potential for improving the conventional mobile learning system and enhancing students' learning performance. The findings of this study can provide a reference for future research on the possibilities of using wearable devices for museum education. [ABSTRACT FROM AUTHOR]

At the forefront of technological advancement, the proliferation of portable and wearable electronics has necessitated the development of innovative power solutions. As these devices become increasingly indispensable in daily life, the demand for sustainable and adaptable power sources has intensified. This review focuses on integrated self‐charging power systems (SCPSs), which synergize energy storage systems, particularly through rechargeable batteries like lithium‐ion batteries, with energy harvesting from solar, mechanical, thermal, and chemical energy. These SCPSs extend operational times, reduce recharging frequency, and have the potential to develop self‐sufficient power systems. The study explores various approaches to optimize both individual components and the integrated power system for wearable and flexible electronics, covering SCPSs that combine multiple energy‐harvesting strategies. Special attention is given to design considerations, material advancements, and engineering challenges, alongside the latest research breakthroughs in energy harvesting and storage technology. The review concludes with an assessment of the prospects and challenges in the field of battery‐integrated energy harvesting systems, highlighting the need for advancements in energy density, power output, and safety to meet the demands of modern electronics. [ABSTRACT FROM AUTHOR]

Wearable electronics with multi‐functionalities are widely utilized in various domains, including everyday living, healthcare, military training, and sports. Advances in flexible electronic technology, new materials, artificial intelligence technology, and sensor technology have accelerated the rapid development of smart wearable devices toward multifunctional and highly integrated trends. The energy supply technology based on the human‐body energy harvesting method endows wearable, multifunctional electronic devices with sustainable, renewable, and self‐powered characteristics, which proposes a solution strategy for the function expansion and energy supply of wearable devices. Herein, this paper discusses recent research on various methods of harvesting human body energy and wearing parts respectively, focusing on the new materials, structures, and processes involved in the representative studies, as well as the impact on energy harvesting and output, and functional applications. Furthermore, the challenges and obstacles faced in the creation of wearable multifunctional devices based on human self‐sufficiency and propose solution strategies to propel them in order to advance the creation of the next wave of intelligent wearable technology are also discussed. [ABSTRACT FROM AUTHOR]

In nearly 60 years of research on exoskeleton design, manufacturing, testing, and application, few researchers study what goes into the process of designing these devices that augment human performance. Exoskeleton evaluation and selection, however, remains under--researched and ultimately unclear given the large number of decision criteria with possible interdependencies. In this paper, an analytic network process (ANP) model was structured and used due to its ability to consider multi-levels of interdependencies between decision criteria while encapsulating the advantages of expert opinion. A panel of experts categorized 55 engineering design metrics into five categories. Discussion sessions were conducted to identify the metrics' interdependencies yielding an unweighted, weighted, and limit priority super-matrix from the ANP model. The results of the ANP model provide an analysis of how interdependencies affect the importance of design metrics in exoskeleton design. The results of the proposed model were compared to a previous study where a panel of 40 experts individually ranked the importance of engineering design metrics without considering interdependencies. The use of the ANP can provide a stronger, more holistic approach to the inherently multi-criteria decision-making involved in exoskeleton design. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to analyze the sleep patterns of patients with nocturnal enuresis (NE) using a wearable brain-wave-sensing device. Materials and Methods: We prospectively analyzed 10 of the 14 patients who visited the hospital for NE between August 2023 and July 2024 and agreed to participate in the study. Four patients were excluded owing to pairing errors and loss to follow-up. Participants were instructed to maintain a frequency volume chart for 1 week and sleep while wearing MUSE-S™, a wearable brainwave detection device. We monitored the arousal threshold state based on sleep patterns when NE occurred in all patients. Results: The median age of the patients was 8.7 years (interquartile range, 7.0-10.6), and 4 patients (40.0%) were male. In all participants, non-rapid eye movement (NREM) deep sleep was the dominant stage in the early part of sleep, whereas NREM light sleep was dominant in the middle part. The arousal threshold was high in the early part of sleep and decreased toward the end. A poor response to enuretic alarms was observed when NE occurred during the NREM deep-dominant period. In contrast, a good response was observed when NE occurred during the NREM light-dominant period. Conclusions: Differences in sleep stages influence the arousal threshold more than individual characteristics. To effectively manage the NE, focusing on the timing of urination is important in the latter part of the sleep period. [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]

The market for technology that tracks ovulation to promote conception is rapidly expanding in the United States, targeting the growing audience of technologically proficient, reproductive-age female consumers. In this narrative review, 23 different, nonprescription wearables and devices designed to help women track their fertile window were identified as currently, commercially available in the United States. The majority of these utilize measurements of basal body temperature or combinations of various urinary hormones. This clinical opinion characterizes the scant available research validating the accuracy of these technologies. It further examines research oversight, discusses the utility of these wearables and devices to consumers, and considers these technologies through an equity lens. The discussion concludes with a call for innovation, describing promising new technologies that not only harness unique physiologic parameters to predict ovulation, but also focus on cost-effectiveness with the hope of increasing access to these currently costly devices and wearables. [ABSTRACT FROM AUTHOR]

The paper is devoted to the study of perfusion and amplitude‐frequency spectra of laser Doppler flowmetry (LDF) signals in patients with diabetes mellitus (DM) in different skin areas of the upper and lower extremities using a distributed system of wearable LDF analysers. LDF measurements were performed in the areas of the fingers, toes, wrists and shins. The mean perfusion values, the amplitudes of blood flow oscillations in endothelial, neurogenic, myogenic, respiratory and cardiac frequency ranges, and the values of nutritive blood flow were analysed. The results revealed a decrease in tissue perfusion and nutritive blood flow in the lower extremities and an increase in these parameters in the upper extremities in patients with DM. A decrease in the amplitudes of endothelial and neurogenic oscillations was observed. The obtained results confirm the possibility of using wearable LDF analysers to detect differences in the blood flow regulation in normal and pathological conditions. [ABSTRACT FROM AUTHOR]

Tactile sensors have garnered considerable interest for their capacity to detect and quantify tactile information. The incorporation of microstructural designs into flexible tactile sensors has emerged as a potent strategy to augment their sensitivity to pressure variations, thereby enhancing their linearity, response spectrum, and mechanical robustness. This review underscores the imperative for progress in microstructured flexible tactile sensors. Subsequently, the discourse transitions to the prevalent materials employed in the fabrication of sensor electrodes, encapsulation layers, and active sensing mediums, elucidating their merits and limitations. In‐depth discussions are devoted to tactile sensors adorned with microstructures, including but not limited to, micropyramids, microhemispheres, micropillars, microporous configurations, microcracks, topological interconnections, multilevel constructs, random roughness, biomimetic microstructures inspired by flora and fauna, accompanied by exemplar studies from each category. Moreover, the utility of flexible tactile sensors within the realm of intelligent environments is explicated, highlighting their application in the monitoring of physiological signals, the detection of sliding motions, and the discernment of surface textures. The review culminates in a critical examination of the paramount challenges and predicaments that must be surmounted to further the development and enhance the functional performance of tactile sensors, paving the way for their integration into advanced sensory systems. [ABSTRACT FROM AUTHOR]

Soft robots have been increasingly utilized as sophisticated tools in physical rehabilitation, particularly for assisting patients with neuromotor impairments. However, many soft robotics for rehabilitation applications are characterized by limitations such as slow response times, restricted range of motion, and low output force. There are also limited studies on the precise position and force control of wearable soft actuators. Furthermore, not many studies articulate how bellow-structured actuator designs quantitatively contribute to the robots' capability. This study introduces a paradigm of upper limb soft actuator design. This paradigm comprises two actuators: the Lobster-Inspired Silicone Pneumatic Robot (LISPER) for the elbow and the Scallop-Shaped Pneumatic Robot (SCASPER) for the shoulder. LISPER is characterized by higher bandwidth, increased output force/torque, and high linearity. SCASPER is characterized by high output force/torque and simplified fabrication processes. Comprehensive analytical models that describe the relationship between pressure, bending angles, and output force for both actuators were presented so the geometric configuration of the actuators can be set to modify the range of motion and output forces. The preliminary test on a dummy arm is conducted to test the capability of the actuators. [ABSTRACT FROM AUTHOR]

Blood pressure measurement is a key indicator of vascular health and a routine part of medical examinations. Given the ability of photoplethysmography (PPG) signals to provide insights into the microvascular bed and their compatibility with wearable devices, significant research has focused on using PPG signals for blood pressure estimation. This study aimed to identify specific clinical PPG features that vary with different blood pressure levels. Through a literature review of 297 publications, we selected 16 relevant studies and identified key time-dependent PPG features associated with blood pressure prediction. Our analysis highlighted the second derivative of PPG signals, particularly the b / a and d / a ratios, as the most frequently reported and significant predictors of systolic blood pressure. Additionally, features from the velocity and acceleration photoplethysmograms were also notable. In total, 29 features were analyzed, revealing novel temporal domain features that show promise for further research and application in blood pressure estimation. [ABSTRACT FROM AUTHOR]

Flexible and stretchable electronics have emerged as a groundbreaking technology with wide-ranging applications, including wearable devices, medical implants, and environmental monitoring systems. Among their numerous applications, hydrogen sensing represents a critical area of research, particularly due to hydrogen's role as a clean energy carrier and its explosive nature at high concentrations. This review paper provides a comprehensive overview of the recent advancements in flexible and stretchable electronics tailored for resistive hydrogen sensing applications. It begins by introducing the fundamental principles underlying the operation of flexible and stretchable resistive sensors, highlighting the innovative materials and fabrication techniques that enable their exceptional mechanical resilience and adaptability. Following this, the paper delves into the specific strategies employed in the integration of these resistive sensors into hydrogen detection systems, discussing the merits and limitations of various sensor designs, from nanoscale transducers to fully integrated wearable devices. Special attention is paid to the sensitivity, selectivity, and operational stability of these resistive sensors, as well as their performance under real-world conditions. Furthermore, the review explores the challenges and opportunities in this rapidly evolving field, including the scalability of manufacturing processes, the integration of resistive sensor networks, and the development of standards for safety and performance. Finally, the review concludes with a forward-looking perspective on the potential impacts of flexible and stretchable resistive electronics in hydrogen energy systems and safety applications, underscoring the need for interdisciplinary collaboration to realize the full potential of this innovative technology. [ABSTRACT FROM AUTHOR]

This study explores the integration of blockchain technology in wearable health devices through the design and development of a Smart Fidget Toy. We aimed to investigate design challenges and opportunities of blockchain-based health devices, examine the impact of blockchain integration user experience, and assess its potential to improve data control and user trust. Using an iterative user-centered design approach, we developed a mid-fidelity prototype of a physical fidget device with a blockchain-based web application. Our key contributions include the design of a fidget toy using blockchain for secure health data management, an iterative development process balancing user needs with blockchain integration challenges, and insights into user perceptions of blockchain wearables for health. We conducted user studies, including a survey (n = 28), focus group (n = 6), interactive wireframe testing (n = 7), and prototype testing (n = 10). Our study revealed high user interest (70%) in blockchain-based data control and sharing features and improved perceived security of data (90% of users) with blockchain integration. However, we also identified challenges in user understanding of blockchain concepts, necessitating additional support. Our smart contract, deployed on the Polygon zkEVM testnet, efficiently manages data storage and retrieval while maintaining user privacy. This research advances the understanding of blockchain applications in health wearables, offering valuable insights for the future development of this field. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment is associated with many neurodegenerative diseases. It is essential to detect mild cognitive impairment on time to reduce the prevalence of such disorders. Nevertheless, present clinically employed test scales and biomarkers are time-consuming, user-unfriendly, and expensive. Hence, we developed a neuropsychological test system based on virtual reality in this study, the Virtual Reality Neuropsychological Mild Cognitive Impairment Test (VRNPT). The diagnosis and classification of MCI were achieved by effectively combining digital cognitive parameters and EEG signal features obtained during the VRNPT cognitive task. The VRNPT contains three head-mounted display-based cognitive tasks that assess participants' attention, memory, spatial perception, working memory, and visuospatial executive ability across multiple cognitive domains of functioning. We investigated how to design and optimize these tasks. We conducted a field study by recruiting 80 participants (40 MCI patients and 40 normal older adults). The results showed that the classification accuracy of combining digitized cognitive parameters and EEG signals during VRNPT was 91.3%, higher than using only digitized parameters from VRNPT and applying EEG signals alone, demonstrating the validity and feasibility of this method for diagnosing MCI. The user satisfaction survey showed that the subjects were satisfied with VRNPT. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Effective treatment of bipolar disorder (BD) requires prompt response to mood episodes. Preliminary studies suggest that predictions based on passive sensor data from personal digital devices can accurately detect mood episodes (e.g., between routine care appointments), but studies to date do not use methods designed for broad application. This study evaluated whether a novel, personalized machine learning approach, trained entirely on passive Fitbit data, with limited data filtering could accurately detect mood symptomatology in BD patients.We analyzed data from 54 adults with BD, who wore Fitbits and completed bi‐weekly self‐report measures for 9 months. We applied machine learning (ML) models to Fitbit data aggregated over two‐week observation windows to detect occurrences of depressive and (hypo)manic symptomatology, which were defined as two‐week windows with scores above established clinical cutoffs for the Patient Health Questionnaire‐8 (PHQ‐8) and Altman Self‐Rating Mania Scale (ASRM) respectively.As hypothesized, among several ML algorithms, Binary Mixed Model (BiMM) forest achieved the highest area under the receiver operating curve (ROC‐AUC) in the validation process. In the testing set, the ROC‐AUC was 86.0% for depression and 85.2% for (hypo)mania. Using optimized thresholds calculated with Youden's J statistic, predictive accuracy was 80.1% for depression (sensitivity of 71.2% and specificity of 85.6%) and 89.1% for (hypo)mania (sensitivity of 80.0% and specificity of 90.1%).We achieved sound performance in detecting mood symptomatology in BD patients using methods designed for broad application. Findings expand upon evidence that Fitbit data can produce accurate mood symptomatology predictions. Additionally, to the best of our knowledge, this represents the first application of BiMM forest for mood symptomatology prediction. Overall, results move the field a step toward personalized algorithms suitable for the full population of patients, rather than only those with high compliance, access to specialized devices, or willingness to share invasive data. [ABSTRACT FROM AUTHOR]

Featured Application: This work allows for ultrasound imaging with wearable probes by roughly attaching them to the skin, facilitating the monitoring of the body internal tissues in daily life. Skin-attachable ultrasound probes face challenges in imaging the intended cross-section due to the difficulty in precisely adjusting the position and angle of attachment. While matrix element arrays are capable of imaging any cross-section within a three-dimensional field of view, their implementation presents a challenge due to the significant number of required ultrasound elements. We propose a method for optimizing the coordinates and shapes of elements based on the focusing quality onto the imaging points under the positional and angular errors in the element array. A 128-element array was optimized through the proposed method and its imaging performance was evaluated with simulated phantoms. The optimized array demonstrated the ability to clearly visualize the simulated wires, cysts, and blood vessels even with the positional error of 3 mm and the angular error of 20°. These results indicate the feasibility of developing a skin-attachable ultrasound probe that can be easily used in daily life without requiring precise positional and angular accuracy. [ABSTRACT FROM AUTHOR]

Disease outbreaks are nowadays a critical issue despite the development and rapid growth of technology. One of the major challenges facing healthcare professionals and healthcare industries is disease prevention and control by technology. Due to busy work schedules, maintaining a healthy lifestyle has become impossible, so the solution to these previous challenges is the intelligent health monitoring system. Over the past few years, a lot of research has been done on the use of Machine learning (ML) techniques in healthcare applications. With wearable devices, ML greatly helps in tracking human activities and vital signs as well as helping to monitor and diagnose patients' health so it plays a huge role in elderly care. Research and development of more applications for wearable devices has been underway in the past five years, due to significant technological advances in medical sensors. Despite the widespread use of wearable devices, there is little research on machine learning applications of these devices. This paper presents a review of the different areas of recent ML research for healthcare wearable devices. It also discusses the different challenges facing ML applications on wearable devices. [ABSTRACT FROM AUTHOR]

Introduction: The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices. Methods: The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis. Results: The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project. Discussion: Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios. [ABSTRACT FROM AUTHOR]

This summary discusses the integration of EEG biomarkers in the neurocognitive screening of executive functions in substance and behavioral addiction. It emphasizes the importance of understanding neurocognitive impairment in addiction to develop effective assessment and treatment strategies. The article suggests using standardized procedures for screening that include computerized cognitive tasks and EEG data collection, as well as wearable devices for continuous monitoring and personalized treatment. While wearables have the potential to enhance addiction treatment, challenges such as privacy concerns and cost need to be addressed. The summary also mentions a separate study on the use of wearable EEG devices to detect distractions during motor rehabilitation, which found promising results. [Extracted from the article]

This paper presents a real-time wearable system designed to assist Parkinson's disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide 'on demand' vibratory stimulation to patients. This paper examines the system's ability to operate with minimal latency, achieving an average detection delay of just 261 milliseconds and a freezing of gait detection accuracy of 95.1%. While patients received on-demand stimulation, the system's effectiveness was assessed by decreasing the average duration of freezing of gait episodes by 45%. These preliminarily results underscore the potential of personalized, real-time feedback systems in enhancing the quality of life and rehabilitation outcomes for patients with movement disorders. [ABSTRACT FROM AUTHOR]

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies. [ABSTRACT FROM AUTHOR]

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring. [ABSTRACT FROM AUTHOR]

Summary Sleep is vital for health. It has regenerative and protective functions. Its disruption reduces the quality of life and increases susceptibility to disease. During sleep, there is a cyclicity of distinct phases that are studied for clinical purposes using polysomnography (PSG), a costly and technically demanding method that compromises the quality of natural sleep. The search for simpler devices for recording biological signals at home addresses some of these issues. We have reworked a single‐channel in‐ear electroencephalography (EEG) sensor grounded to a commercially available memory foam earplug with conductive tape. A total of 14 healthy volunteers underwent a full night of simultaneous PSG, in‐ear EEG and actigraphy recordings. We analysed the performance of the methods in terms of sleep metrics and staging. In another group of 14 patients evaluated for sleep‐related pathologies, PSG and in‐ear EEG were recorded simultaneously, the latter in two different configurations (with and without a contralateral reference on the scalp). In both groups, the in‐ear EEG sensor showed a strong correlation, agreement and reliability with the ‘gold standard’ of PSG and thus supported accurate sleep classification, which is not feasible with actigraphy. Single‐channel in‐ear EEG offers compelling prospects for simplifying sleep parameterisation in both healthy individuals and clinical patients and paves the way for reliable assessments in a broader range of clinical situations, namely by integrating Level 3 polysomnography devices. In addition, addressing the recognised overestimation of the apnea‐hypopnea index, due to the lack of an EEG signal, and the sparse information on sleep metrics could prove fundamental for optimised clinical decision making. [ABSTRACT FROM AUTHOR]

Post COVID-19 infections resulting in long COVID symptoms remain persistent yet neglected in healthcare priorities. Although long COVID symptoms are expected to decline after some time, many people continue to endure its debilitating effects affecting their daily lives. The diversity of characteristics amongst long COVID patients adds to the complexity of communicating personal health predicaments to healthcare providers. Recent research towards building an evidence base for long COVID with the aim of delivering responsive healthcare interventions for long COVID patients has utilised datasets generated from wearable devices. This paper examines the prospects presented by wearable devices for long COVID patients and physicians. It highlights distinct ethical and legal challenges arising from their use in practice. Several recommendations aiming to support their usage amongst long COVID patients are outlined for future research using wearable devices for long COVID treatment. [ABSTRACT FROM AUTHOR]

Flexible and stretchable strain sensors are in high demand in sports performance monitoring, structural health monitoring, and biomedical applications. However, existing stretchable soft sensors, primarily based on soft polymer materials, often suffer from drawbacks, including high hysteresis, low durability, and delayed response. To overcome these limitations, a stretchable miniature fiber sensor comprised of a stretchable core tightly coiled with parallel conductive wires is introduced. This fiber sensor is flexible and stretchable while exhibiting low hysteresis, a remarkable theoretical resolution of 0.015%, a response time of <30 milliseconds, and excellent stability after extensive cycling tests of over 16 000 cycles. To understand and predict the capacitive sensor response of the proposed sensor, an analytical expression is derived and proved to have good agreements with both experimental results and numerical simulation. The potential of the strain sensor as a wearable device is demonstrated by embedding it into belts, gloves, and knee protectors. Additionally, the sensor can extend its applications beyond wearable devices, as demonstrated by its integration into bladder and life safety rope monitoring systems. The sensor is envisioned to have applications in the field of sports performance evaluations, health care monitoring, and structural safety assessments. [ABSTRACT FROM AUTHOR]

The conventional medical assistive strategy for wearable devices involves sensing physiological signals and subsequent analysis while it lacks the capability for visual interaction and often necessitates the use of additional bulky devices for display. Herein, a perceptual and interaction strategy based on electroluminescent and triboelectric sensing 3D stacked wearable device (ETD) is proposed to address current issues. The ETD utilizes a bottom triboelectric sensor for tactile physiological information, which is processed and analyzed, ultimately triggering the selective illumination of the top electroluminescent array through external power circuitry. The ETD possesses the capability to generate a 100 V with 1.5 cm2 and stable visual interaction ability at 30 V drive voltage. Based on outstanding features of the ETD, two medical assistance scenarios are explored. I) The ETD captures physiological signals, such as pulse rate, respiratory rate, and special joint flexion, and in situ displays the corresponding physiological information, effectively assisting the patients/elders in self‐monitoring. II) A bidirectional interactive telemedicine assistive system utilizing ETDs is implemented, which not only gathers real‐time activity status from patients/elders but also realizes that remote physician assesses the safety status and the assessment results are wirelessly back‐communicated to the ETD, enabling real‐time interaction of the physician advice. [ABSTRACT FROM AUTHOR]

Rare neurological diseases as a whole share peculiar features as motor and/or cognitive impairment, an elevated disability burden, a frequently chronic course and, in present times, scarcity of therapeutic options. The rarity of those conditions hampers both the identification of significant prognostic outcome measures, and the development of novel therapeutic approaches and clinical trials. Collection of objective clinical data through digital devices can support diagnosis, care, and therapeutic research. We provide an overview on recent developments in the field of digital tools applied to rare neurological diseases, both in the care setting and as providers of outcome measures in clinical trials in a representative subgroup of conditions, including ataxias, hereditary spastic paraplegias, motoneuron diseases and myopathies. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the effects of a head/neck supporting exoskeleton (HNSE) on the electromyographic fatigue threshold (EMGFT) of the neck and shoulder muscles during a simulated overhead work task. Background: Overhead work is a well-known risk factor for neck and shoulder musculoskeletal disorders due to the excessive strain imposed on the muscles and joints in these regions. Method: Fourteen healthy males performed a repetitive overhead nut fastening/unfastening task to exhaustion while wearing and not wearing the HNSE at two neck extension angles (40% and 80% of neck maximum range of motion). Electromyographic signals were continuously recorded from the right and left sternocleidomastoid (SCMR, SCML), splenius capitis (SCR, SCL), upper trapezius (UTR, UTL), and anterior deltoid (ADR, ADL) muscles. The normalized electromyographic amplitude (nEMG) data was time normalized, and a bisegmental linear regression was applied to determine the muscle fatigue break point. Results: The results showed a significant increase in fatigue threshold time in the SCMR (p <.001), SCML (p =.002), and UTR (p =.037) muscles when the HNSE was used. However, the EMGFT times for the right and left deltoid and left trapezius muscles showed a nonsignificant reduction due to the head/neck support exoskeleton use. In addition, the neck extension angle did not reveal a significant effect on muscles' EMGFT time. Conclusion: Overall, the findings confirmed a significant delay in fatigue onset in sternocleidomastoid muscles, as measured by the electromyographic fatigue threshold. This finding suggests that the HNSE can be an effective ergonomic intervention for reducing the risk of musculoskeletal disorders in overhead workers. However, further studies are needed to investigate the effect of the HNSE at other neck extension angles and more realistic tasks to ensure the generalizability of our results. Application: The present findings emphasize the application of the fatigue onset time to evaluate the effectiveness of ergonomic interventions, including exoskeletons, which can subsequently be utilized to alleviate postural demands and reduce the risk of musculoskeletal disorders. [ABSTRACT FROM AUTHOR]

Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18–64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = –.27, p =.03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98–1.00 vs. 0.75–0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95–0.99 vs. 0.85–0.98; concurrent validity: 0.95–1.00 vs. 0.75–0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99–1.00 vs. 0.89–0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest. [ABSTRACT FROM AUTHOR]

Wearable devices have the potential to advance health care by enabling real-time monitoring of biobehavioral data and facilitating the management of an individual's health conditions. Individuals living with spinal cord injury (SCI) have impaired motor function, which results in deconditioning and worsening cardiovascular health outcomes. Wearable devices may promote physical activity and allow the monitoring of secondary complications associated with SCI, potentially improving motor function, sleep, and cardiovascular health. However, several challenges remain to optimize the application of wearable technologies within this population. One is striking a balance between research-grade and consumer-grade devices in terms of cost, accessibility, and validity. Additionally, limited literature supports the validity and use of wearable technology in monitoring cardio-autonomic and sleep outcomes for individuals with SCI. Future directions include conducting performance evaluations of wearable devices to precisely capture the additional variation in movement and physiological parameters seen in those with SCI. Moreover, efforts to make the devices small, lightweight, and inexpensive for consumer ease of use may affect those with severe motor impairments. Overcoming these challenges holds the potential for wearable devices to help individuals living with SCI receive timely feedback to manage their health conditions and help clinicians gather comprehensive patient health information to aid in diagnosis and treatment. [ABSTRACT FROM AUTHOR]

In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients' health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare. [ABSTRACT FROM AUTHOR]

Falls, wheelchair dependence, and bedridden status are the results of reduced mobility in the mid-late course of dementia. Kinematic gait analysis for patients with dementia is lacking because practically setting sensors on their bodies is particularly difficult. We analyzed the parameters of kinematic gait analysis that are related to the risks of wheelchair dependence in patients with dementia using wearable accelerometers and gyroscopes for detecting 3-dimensional physical movements. We collected data from 34 patients with dementia regarding demographics, cognitive function, CT scan findings, medications, and gait analysis parameters. The patients were followed up for 6 months. We compared data between dementia patients with and without wheelchair dependence by t-test or Fisher's exact test, multiple comparison, and simple logistic regression analysis for wheelchair dependence by gait analysis parameters. Eleven patients became wheelchair-dependent during the 6 months. The score on the clinical dementia rating scale was significantly higher and the hip extensor angle in walking was significantly lower in patients with dementia with wheelchair dependence than in those without. The severity of dementia and the lower angle of the hip extensor during walking may indicate the necessity of a wheelchair for patients with this disease. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is an acute and degenerative condition with high morbidity and mortality rates. Early diagnosis and treatment of HF can significantly enhance patient outcomes through admission and readmission reduction and improve quality of life. Being a progressive condition, the continuous monitoring of vital signs and symptoms of HF patients to identify any deterioration and to customize treatment regimens can be beneficial to the management of this disease. Recent breakthroughs in wearable technology have revolutionized the landscape of HF management. Despite the potential benefits, the integration of wearable devices into HF management requires careful consideration of technical, clinical, and ethical challenges, such as performance, regulatory requirements and data privacy. This review summarizes the current evidence on the role of wearable devices in heart failure monitoring and management, and discusses the challenges and opportunities in the field. [ABSTRACT FROM AUTHOR]

Background: The number of individuals living with multiple (≥2) long term conditions (MLTCs) is a growing global challenge. People with MLTCs experience reduced life expectancy, complex healthcare needs, higher healthcare utilisation, increased burden of treatment, poorer quality of life and higher mortality. Evolving technologies including artificial intelligence (AI) could address some of these challenges by enabling more preventive and better integrated care, however, they may also exacerbate inequities. Objective: We aim to deliver an equity focused, action-ready plan for transforming MLTC prevention and care, co-designed by people with lived experience of MLTCs and delivered through an Innovation Hub: SysteMatic. Design: Our Hub is being co-designed by people with lived experience of MLTCs, practitioners, academics and industry partners in Liverpool and Glasgow, UK. This work builds on research into mental-physical health interdependence across the life-course, and on mobilisation of large-scale quantitative data and technology validation in health and care systems serving deprived populations in Glasgow and Liverpool. We work with 3 population segments: 1) Children & Families: facing psychosocial and environmental challenges with lifetime impacts; 2). Working Life: people with poorly integrated mental, physical and social care; and 3) Pre-Frailty: older people with MLTCs. We aim to understand their experiences and in parallel look at routinely collected health data on people with MLTCs to help us identify targets for intervention. We are co-identifying opportunities for systems transformation with our patient partners, healthcare professionals and through discussion with companies and public-sector organisations. We are co-defining 3/5/7-year MLTC innovation/transition targets and sustainable learning approaches. Discussion: SysteMatic will deliver an actionable MLTC Innovation Hub strategic plan, with investment from the UK National Health Service, civic health and care partners, universities, and industry, enabling feedback of well-translated, patient and public prioritised problems into the engineering, physical, health and social sciences to underpin future equitable innovation delivery. [ABSTRACT FROM AUTHOR]

Due to its outstanding physical and chemical properties, graphene synthesized by laser scribing on polyimide (PI) offers excellent opportunities for photothermal applications, antiviral and antibacterial surfaces, and electrochemical storage and sensing. However, the utilization of such graphene for imaging is yet to be explored. Herein, using chemically durable and electrically conductive laser‐induced graphene (LIG) for tomography imaging in aqueous suspensions is proposed. These graphene electrodes are designed as impedance imaging units for four‐terminal electrical measurements. Using the real‐time portable imaging prototypes, the conductive and dielectric objects can be seen in clear and muddy water with equivalent impedance modeling. This low‐cost graphene tomography measurement system offers significant advantages over traditional visual cameras, in which the suspended muddy particles hinder the imaging resolution. This research shows the potential of applying graphene nanomaterials in emerging marine technologies, such as underwater robotics and automatic fisheries. [ABSTRACT FROM AUTHOR]

Laboratory medicine has undergone a deep and multifaceted revolution in the course of human history, in both organizational and technical terms. Over the past century, there has been a growing recognition of the need to centralize numerous diagnostic activities, often similar or identical but located in different clinical departments, into a common environment (i.e., the medical laboratory service), followed by a progressive centralization of tests from smaller laboratories into larger diagnostic facilities. Nevertheless, the numerous technological advances that emerged at the beginning of the new millennium have helped to create a new testing culture characterized by a countervailing trend of decentralization of some tests closer to patients and caregivers. The forces that have driven this (centripetal) counter-revolution essentially include a few key concepts, namely "home testing", "portable or even wearable devices" and "remote patient monitoring". By their very nature, laboratory medical services and remote patient testing/monitoring are not contradictory, but may well coexist, with the choice of one or the other depending on the demographic and clinical characteristics of the patient, the type of analytical procedure and the logistics and local organization of the care system. Therefore, this article aims to provide a general overview of patient self-testing, with a particular focus on portable and wearable (including implantable) devices. [ABSTRACT FROM AUTHOR]

In the evolving field of human–computer interaction (HCI), gesture recognition has emerged as a critical focus, with smart gloves equipped with sensors playing one of the most important roles. Despite the significance of dynamic gesture recognition, most research on data gloves has concentrated on static gestures, with only a small percentage addressing dynamic gestures or both. This study explores the development of a low-cost smart glove prototype designed to capture and classify dynamic hand gestures for game control and presents a prototype of data gloves equipped with five flex sensors, five force sensors, and one inertial measurement unit (IMU) sensor. To classify dynamic gestures, we developed a neural network-based classifier, utilizing a convolutional neural network (CNN) with three two-dimensional convolutional layers and rectified linear unit (ReLU) activation where its accuracy was 90%. The developed glove effectively captures dynamic gestures for game control, achieving high classification accuracy, precision, and recall, as evidenced by the confusion matrix and training metrics. Despite limitations in the number of gestures and participants, the solution offers a cost-effective and accurate approach to gesture recognition, with potential applications in VR/AR environments. [ABSTRACT FROM AUTHOR]

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases. [ABSTRACT FROM AUTHOR]

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]

Individuals with Down Syndrome exhibit deficits in muscle strength and cardiovascular adaptation, which limit athletic performance. We compared a maximum-intensity 50 m front crawl test between competitive male swimmers with Down Syndrome (SDS; n = 11; 26.5 ± 5.6 years; m ± SD) and a control group of swimmers (CNT; n = 11; 27.1 ± 4.0 years) with similar training routines (about 5 h/week). Wearable sternal sensors measured their heart rate and 3D accelerometry. The regularity index Sample Entropy (SampEn) was calculated using the X component of acceleration. The total times (SDS: 58.91 ± 13.68 s; CNT: 32.55 ± 3.70 s) and stroke counts (SDS: 66.1 ± 9.6; CNT: 51.4 ± 7.4) were significantly higher in the SDS group (p < 0.01). The heart rate was lower in the SDS group during immediate (SDS: 129 ± 15 bpm; CNT: 172 ± 11 bpm) and delayed recovery (30 s, SDS: 104 ± 23 bpm; CNT: 145 ± 21 bpm; 60 s, SDS: 79 ± 27 bpm; CNT: 114 ± 27 bpm) (p < 0.01 for all the comparisons). The SampEn of sternal acceleration showed no differences between the groups and between 0–25 m and 25–50 m. Body pitch correlated strongly with performance in the SDSs (R2 = 0.632, p < 0.01), but during the first 25 m only. The high-intensity front crawl performances differed between the SDS and CNT athletes in terms of time, biomechanics, and training adaptation, suggesting the need for tailored training to improve swimming efficiency in SDSs. [ABSTRACT FROM AUTHOR]

Background/Objectives: This study aimed to develop a predictive algorithm for the early diagnosis of dementia in the high-risk group of older adults using artificial intelligence technologies. The objective is to create an accessible diagnostic method that does not rely on traditional medical equipment, thereby improving the early detection and management of dementia. Methods: Lifelog data from wearable devices targeting this high-risk group were collected from the AI Hub platform. Various indicators from these data were analyzed to develop a dementia diagnostic model. Machine learning techniques such as Logistic Regression, Random Forest, LightGBM, and Support Vector Machine were employed. Data augmentation techniques were applied to address data imbalance, thereby enhancing the model performance. Results: Data augmentation significantly improved the model's accuracy in classifying dementia cases. Specifically, in gait data, the SVM model performed with an accuracy of 0.879. In sleep data, a Logistic Regression was performed, yielding an accuracy of 0.818. This indicates that the lifelog data can effectively contribute to the early diagnosis of dementia, providing a practical solution that can be easily integrated into healthcare systems. Conclusions: This study demonstrates that lifelog data, which are easily collected in daily life, can significantly enhance the accessibility and efficiency of dementia diagnosis, aiding in the effective use of medical resources and potentially delaying disease progression. [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]

Visible light is an important energy source for all living organisms on Earth. Given the importance of visible light, visible light sensors have attracted widespread interest from scientists. With the rapid development of wearable devices, the sensors used in them need to be flexible, stretchable, and lightweight. Herein, an intelligent electrolyte based on spiropyran (SP) that responds to visible light is developed. The reversible change rate in the electrical resistance of an SP/FeCl3·6H2O methyl cyanide (MeCN) aqueous solution under visible light irradiation is as high as 19.26%. Additionally, flexible and conductive fibrous visible light sensors with a core‐sheath structure are prepared using an SP/FeCl3·6H2O MeCN aqueous solution and silicon rubber hollow fibers as the core and outer layers, respectively. These fibrous visible light sensors are then woven into fabrics with multiple functions, such as sensing and locating visible light, reversible photochromism, and thermal management. The fibrous visible light sensors and fabrics prepared in this study have broad development prospects and application potential in the fields of fashion, smart textiles, flexible conductive fibers, flexible fibrous sensors, electronic skins, and wearable devices. [ABSTRACT FROM AUTHOR]

Musculoskeletal disorders are common among older adults, affecting mobility and quality of life. Effective rehabilitation is essential, but the implementation of programs faces challenges. Traditional methods often necessitate in-person assessments, which can be difficult for older adults with mobility limitations. Telerehabilitation offers a solution, bringing therapy closer to patients. However, the accurate remote monitoring of health and performance remains a challenge. This study addresses this gap by developing and validating the System for Tracking and Evaluating Performance (STEP). STEP is a hardware-software system that automates physical performance tests, eliminating the need for constant expert supervision. The system focuses on three standard tests: the Six-Minute Walking Test (6MWT), the Ten-Meter Walking Test (10MWT), and the 30-s Sit-to-Stand Test (30STS). Validation compared results from the STEP app with in-person assessments by physicians for patients undergoing rehabilitation after knee or hip arthroplasty. The study found strong positive correlations between the app's results and the physicians' assessments for all tests. These findings demonstrate the STEP system's potential as a reliable tool for remote physical performance assessment. Further research is needed to explore its integration into clinical practice and cost-effectiveness in reducing the need for operator assistance in monitoring patients with physical limitations. [ABSTRACT FROM AUTHOR]