Search Results (7,813)

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. Full article

In an aging society, the need for efficient emergency detection systems in smart homes is becoming increasingly important. For elderly people living alone, technical solutions for detecting emergencies are essential to receiving help quickly when needed. Numerous solutions already exist based on wearable or ambient sensors. However, existing methods for emergency detection typically assume that sensor data are error-free and contain no false positives, which cannot always be guaranteed in practice. Therefore, we present a novel method for detecting emergencies in private households that detects unusually long inactivity periods and can process erroneous or uncertain activity information. We introduce the Inactivity Score, which provides a probabilistic weighting of inactivity periods based on the reliability of sensor measurements. By analyzing historical Inactivity Scores, anomalies that potentially represent an emergency can be identified. The proposed method is compared with four related approaches on seven different datasets. Our method surpasses existing approaches when considering the number of false positives and the mean time to detect emergencies. It achieves an average detection time of approximately 05:23:28 h with only $0.09$ false alarms per day under noise-free conditions. Moreover, unlike related approaches, the proposed method remains effective with noisy data. Full article

Owing to their unique physicochemical properties, MXenes have emerged as promising materials for biosensing applications. This review paper comprehensively explores the recent advancements in MXene-based biosensors for health and environmental applications. This review begins with an introduction to MXenes and biosensors, outlining various types of biosensors including electrochemical, enzymatic, optical, and fluorescent-based systems. The synthesis methods and characteristics of MXenes are thoroughly discussed, highlighting the importance of these processes in tailoring MXenes for specific biosensing applications. Particular attention is given to the development of electrochemical MXene-based biosensors, which have shown remarkable sensitivity and selectivity in detecting various analytes. This review then delves into enzymatic MXene-based biosensors, exploring how the integration of MXenes with enzymes enhances sensor performance and expands the range of detectable biomarkers. Optical biosensors based on MXenes are examined, focusing on their mechanisms and applications in both healthcare and environmental monitoring. The potential of fluorescent-based MXene biosensors is also investigated, showcasing their utility in imaging and sensing applications. In addition, MXene-based potential wearable biosensors have been discussed along with the role of MXenes in volatile organic compound (VOC) detection for environmental applications. Finally, this paper concludes with a critical analysis of the current state of MXene-based biosensors and provides insights into future perspectives and challenges in this rapidly evolving field. Full article

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. Full article

The real-time data collected by wearable devices enables personalized health management and supports public health monitoring. However, sharing these data with third-party organizations introduces significant privacy risks. As a result, protecting and securely sharing wearable device data has become a critical concern. This paper proposes a local differential privacy-preserving algorithm designed for continuous data streams generated by wearable devices. Initially, the data stream is sampled at key points to avoid prematurely exhausting the privacy budget. Then, an adaptive allocation of the privacy budget at these points enhances privacy protection for sensitive data. Additionally, the optimized square wave (SW) mechanism introduces perturbations to the sampled points. Afterward, the Kalman filter algorithm is applied to maintain data flow patterns and reduce prediction errors. Experimental validation using two real datasets demonstrates that, under comparable conditions, this approach provides higher data availability than existing privacy protection methods for continuous data streams. Full article

Assessing respiratory frequency (f_R) is practical in monitoring training progress in competitive athletes, especially during exercise. This study aimed to validate a new wearable chest strap (wCS) to estimate f_R against ergospirometry as a criterion device in soccer players. A total of 26 elite professional soccer players (mean [standard deviation]: 23.6 [4.8] years; 180.6 [5.7] cm; 77.2 [5.4] kg) from three Italian Serie A League teams participated in this cross-sectional study. The sample included attackers, midfielders, and defenders. f_R was assessed during a maximal cardiopulmonary exercise test (CPET) on a treadmill using (i) a breath-by-breath gas exchange analyzer (Vyntus^® CPX, Vyaire Medical) and (ii) a novel wCS with sensors designed to assess breath frequency following chest expansions. Pearson’s correlation coefficient (r), adjusted coefficient of determination (aR²), Bland–Altman plot analysis, and Lin’s concordance correlation coefficient (ρ_c) were used for comparative analysis (correlation and concordance) among the methods. The repeated measures correlation coefficient (r_rm) was used to assess the strength of the linear association between the methods. The intraclass correlation coefficient (ICC) and the Finn coefficient (r_F) were used for inter-rater reliability. All statistical analyses were performed within the R statistical computing environment, with 95% confidence intervals (95% CIs) reported and statistical significance set at p < 0.05. A total of 16529 comparisons were performed after collecting the CPET data. The robust time series analysis with Hodges–Lehmann estimation showed no significant differences between both methods (p > 0.05). Correlation among devices was statistically significant and very large (r [95% CI]: 0.970 [0.970, 0.971], p < 0.01; aR² [95% CI]: 0.942 [0.942, 0.943], p < 0.01) with strong evidence supporting consistency of the new wCS (BF₁₀ > 100). In addition, a high concordance was found (ρ_c [95% CI]: 0.970 [0.969, 0.971], bias correction factor: 0.999). Vyntus^TM CPX, as a standard criterion, showed moderate agreement with wCS after Bland–Altman analysis (bias [95% lower to the upper limit of agreement]; % agree: 0.170 [−4.582 to 4.923] breaths·min⁻¹; 69.9%). A strong association between measurements (r_rm [95% CI]: 0.960 [0.959, 0.961]), a high absolute agreement between methods (ICC [95% CI]: 0.970 [0.970, 0.971]), and high inter-rater reliability (r_F: 0.947) were found. With an RMSE = 2.42 breaths·min⁻¹, the new wCS seems to be an valid and reliable in-field method to evaluate f_R compared to a breath-by-breath gas exchange analyzer. Notwithstanding, caution is advised if methods are used interchangeably while further external validation occurs. Full article

Sweat is considered the most promising candidate to replace conventional blood samples for noninvasive sensing. There are many tools and optical and electrochemical methods that can be used for detecting sweat biomarkers. Electrochemical methods are known for their simplicity and cost-effectiveness. However, they need to be optimized in terms of selectivity and catalytic activity. Therefore, electrode modifiers such as nanostructures and metal–organic frameworks (MOFs) or combinations of them were examined for boosting the performance of the electrochemical sensors. The MOF structures can be prepared by hydrothermal/solvothermal, sonochemical, microwave synthesis, mechanochemical, and electrochemical methods. Additionally, MOF nanostructures can be prepared by controlling the synthesis conditions or mixing bulk MOFs with nanoparticles (NPs). In this review, we spotlight the previously examined MOF-based nanostructures as well as promising ones for the electrochemical determination of sweat biomarkers. The presence of NPs strongly improves the electrical conductivity of MOF structures, which are known for their poor conductivity. Specifically, Cu-MOF and Co-MOF nanostructures were used for detecting sweat biomarkers with the lowest detection limits. Different electrochemical methods, such as amperometric, voltammetric, and photoelectrochemical, were used for monitoring the signal of sweat biomarkers. Overall, these materials are brilliant electrode modifiers for the determination of sweat biomarkers. Full article

Ocular artifacts, including blinks and saccades, pose significant challenges in the analysis of electroencephalographic (EEG) data, often obscuring crucial neural signals. This tutorial provides a comprehensive guide to the most effective methods for correcting these artifacts, with a focus on algorithms designed for both laboratory and real-world settings. We review traditional approaches, such as regression-based techniques and Independent Component Analysis (ICA), alongside more advanced methods like Artifact Subspace Reconstruction (ASR) and deep learning-based algorithms. Through detailed step-by-step instructions and comparative analysis, this tutorial equips researchers with the tools necessary to maintain the integrity of EEG data, ensuring accurate and reliable results in neurophysiological studies. The strategies discussed are particularly relevant for wearable EEG systems and real-time applications, reflecting the growing demand for robust and adaptable solutions in applied neuroscience. Full article

In the domain of healthcare, wearable health monitoring devices have emerged as essential tools for the advancement of patient health tracking. These devices facilitate the provision of real-time medical data to clinicians, enabling early diagnosis, timely intervention, and enhanced management of individual health. This study introduces an innovative trimodal wearable health monitoring device in the form of a wristwatch. The device integrates a breath analyzer for the assessment of gaseous phase biomarkers, a sweat analyzer for the evaluation of aqueous-phase biomarkers, and an infrared sensor for the measurement of body temperature in the optical phase. Engineered on a compact 3 cm × 3 cm printed circuit board, the device has been optimized for wearability, power efficiency, and seamless integration with both wired and wireless charging and communication systems. Furthermore, custom software applications, designed for both Windows and Android platforms, have been developed to facilitate intuitive data visualization and storage on personal computers and smartphones. Empirical results from real-time chemical testing substantiate the device’s efficacy and potential as an advanced solution for wearable health monitoring. Full article

With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time–frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources. Full article

Hand paralysis, caused by conditions such as spinal cord injuries, strokes, and arthritis, significantly hinders daily activities. Wearable exo-gloves and telerehabilitation offer effective hand training solutions to aid the recovery process. This study presents the development of lightweight wearable exo-gloves designed for finger telerehabilitation. The prototype uses NiTi shape memory alloy (SMA) actuators to control five fingers. Specialized end effectors target the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints, mimicking human finger tendon actions. A variable structure controller, managed through a web-based Human–Machine Interface (HMI), allows remote adjustments. Thermal behavior, dynamics, and overall performance were modeled in MATLAB Simulink, with experimental validation confirming the model’s efficacy. The phase transformation characteristics of NiTi shape memory wire were studied using the Souza–Auricchio model within COMSOL Multiphysics 6.2 software. Comparing the simulation to trial data showed an average error of 2.76°. The range of motion for the MCP, PIP, and DIP joints was 21°, 65°, and 60.3°, respectively. Additionally, a minimum torque of 0.2 Nm at each finger joint was observed, which is sufficient to overcome resistance and meet the torque requirements. Results demonstrate that integrating SMA actuators with telerehabilitation addresses the need for compact and efficient wearable devices, potentially improving patient outcomes through remote therapy. Full article

Photodetectors are critical components in a wide range of applications, from imaging and sensing to communications and environmental monitoring. Recent advancements in material science have led to the development of emerging photodetecting materials, such as perovskites, polymers, novel two-dimensional materials, and quantum dots, which offer unique optoelectronic properties and high tunability. This review presents a comprehensive overview of the synthesis methodologies for these cutting-edge materials, highlighting their potential to enhance photodetection performance. Additionally, we explore the design and fabrication of photodetectors with novel structures and physics, emphasizing devices that achieve high figure-of-merit parameters, such as enhanced sensitivity, fast response times, and broad spectral detection. Finally, we discuss the demonstration of new applications enabled by these advanced photodetectors, including flexible and wearable devices, next-generation imaging systems, and environmental sensing technologies. Through this review, we aim to provide insights into the current trends and future directions in the field of photodetection, guiding further research and development in this rapidly evolving area. Full article

Objectives. This study aimed to compare the audiological benefits of a non-implantable wearable option for a bone conduction (BC) implant mounted on an arch (SoundArc) to those of traditional BC hearing aids (HAs) mounted on eyeglasses (BCHAs) in patients with moderate to severe conductive or mixed hearing loss. Methods: A preliminary cross-sectional observational prospective cohort study was conducted in the Tertiary Audiological Department, University Hospital. Fourteen adults with conductive or mixed hearing loss (PTA at 0.5-1-2-4 KHz = 67 ± 15 dB HL) who had been wearing conventional BCHAs mounted on eyeglasses for at least 3 years and had declined surgical implantation of a bone conduction hearing implant (BCHI) were included in the study. Unaided and aided pure-tone air conduction (AC) and bone conduction (BC) thresholds, as well as speech tests in quiet and noise, were recorded at baseline and in two different settings: with a BCHI mounted on SoundArc^® and with their own BCHAs mounted on eyeglasses using two couplers. Participants completed questionnaires in both conditions, including the International Inventory for Hearing Aids (IOI-HA), the Hearing Handicap Inventory for Adults/Elderly (HHIA/E), the Speech, Spatial, and Qualities of Hearing Scale (SSQ), a 10-point visual analog scale (VAS), and the Fatigue Impact Scale (FIS). Results: A significant functional gain was observed in both settings (p = 0.0001). Better speech perception in quiet and noise was observed with SoundArc compared to conventional BCHAs on eyeglasses (improvements in word repetition scores in noise: +19.3 at SNR +10 dB, p = 0.002; +12.1 at SNR 0 dB, p = 0.006; and +11.4 at SNR −10 dB, p = 0.002). No significant differences were found in IOI-HA, FIS, and HHIA/E scores. However, significantly better SSQ scores were reported for SoundArc in all domains (p = 0.0038). Conclusions: Although patients were accustomed to using BCHAs mounted on eyeglasses, the bone conduction wearable option of the BCHI (SoundArc) proved to be a viable alternative for adult patients with conductive or mixed hearing loss who are unable or unwilling to undergo BCHI surgery. Full article

Personal thermal management materials integrated with phase-change materials have significant potential to satisfy human thermal comfort needs and save energy through the efficient storage and utilization of thermal energy. However, conventional organic phase-change materials in a solid state suffer from rigidity, low thermal conductivity, and leakage, making their application challenging. In this work, polyethylene glycol (PEG) was chosen as the phase-change material to provide the energy storage density, polyethylene oxide (PEO) was chosen to provide the backbone structure of the three-dimensional polymer network and cross-linked with the PEG to provide flexibility, and carbon nanotubes (CNTs) were used to improve the mechanical and thermal conductivity of the material. The thermal conductivity of the composite fiber membranes was boosted by 77.1% when CNTs were added at 4 wt%. Water-resistant modification of the composite fiber membranes was successfully performed using glutaraldehyde-saturated steam. The resulting composite fiber membranes had a reasonable range of phase transition temperatures, and the CC₄PCF-55 membranes had melting and freezing latent heats of 66.71 J/g and 64.74 J/g, respectively. The results of this study prove that the green CC₄PCF-55 composite fiber membranes have excellent flexibility, with good thermal energy storage capacity and thermal conductivity and, therefore, high potential in the field of flexible wearable thermal management textiles. Full article

A new method based on the Recurrence Quantification Analysis (RQA) of the heart rate (HR) offers an objective, efficient alternative to traditional methods for Aerobic Threshold (AerT) identification that have practical limitations due to the complexity of equipment and interpretation. This study aims to validate the RQA-based method’s applicability across varied demographics, exercise protocols, and health status. Data from 123 cardiopulmonary exercise tests were analyzed, and participants were categorized into four groups: athletes, young athletes, obese individuals, and cardiac patients. Each participant’s AerT was assessed using both traditional ventilatory equivalent methods and the automatic RQA-based method. Ordinary Least Products (OLP) regression analysis revealed strong correlations (r > 0.77) between the RQA-based and traditional methods in both oxygen consumption (VO₂) and HR at the AerT. Mean percentage differences in HR were below 2.5%, and the Technical Error for HR at AerT was under 8%. The study validates the RQA-based method, directly applied to HR time series, as a reliable tool for the automatic detection of the AerT, demonstrating its accuracy across diverse age groups and fitness levels. These findings suggest a versatile, cost-effective, non-invasive, and objective tool for personalized exercise prescription and health risk stratification, thereby fulfilling the study’s goal of broadening the method’s applicability. Full article