Search Results (7,568)

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

Instrumenting the six-minute walk test (6MWT) adds information about gait quality and insight into fall risk. Being physically active and preserving multi-directional stepping abilities are also important for fall risk reduction. This analysis investigated the relationship of gait quality during the 6MWT with physical functioning and physical activity. Twenty-one veterans (62.2 ± 6.4 years) completed the four square step test (FSST) multi-directional stepping assessment, a gait speed assessment, health questionnaires, and the accelerometer-instrumented 6MWT. An activity monitor worn at home captured free-living physical activity. Gait measures were not significantly different between minutes of the 6MWT. However, participants with greater increases in stride time (ρ = −0.594, p < 0.01) and stance time (ρ = −0.679, p < 0.01) during the 6MWT reported lower physical functioning. Neither physical activity nor sedentary time were related to 6MWT gait quality. Participants exploring a larger range in stride time variability (ρ = 0.614, p < 0.01) and stance time variability (ρ = 0.498, p < 0.05) during the 6MWT required more time to complete the FSST. Participants needing at least 15 s to complete the FSST meaningfully differed from those completing the FSST more quickly on all gait measures studied. Instrumenting the 6MWT helps detect ranges of gait performance and provides insight into functional limitations missed with uninstrumented administration. Established FSST cut points identify aging adults with poorer gait quality. Full article

One of the significant challenges with traditional single-task learning-based anomaly detection using noisy hyperspectral images (HSIs) is the loss of anomaly targets during denoising, especially when the noise and anomaly targets are similar. This issue significantly affects the detection accuracy. To address this problem, this paper proposes a multitask learning (MTL)-based method for detecting anomalies in noisy HSIs. Firstly, a preliminary detection approach based on the JointNet model, which decomposes the noisy HSI into a pure background and a noise–anomaly target mixing component, is introduced. This approach integrates the minimum noise fraction rotation (MNF) algorithm into an autoencoder (AE), effectively isolating the noise while retaining critical features for anomaly detection. Building upon this, the JointNet model is further optimized to ensure that the noise information is shared between the denoising and anomaly detection subtasks, preserving the integrity of the training data during the anomaly detection process and resolving the issue of losing anomaly targets during denoising. A novel loss function is designed to enable the joint learning of both subtasks under the multitask learning model. In addition, a noise score evaluation metric is introduced to calculate the probability of a pixel being an anomaly target, allowing for a clear distinction between noise and anomaly targets, thus providing the final anomaly detection results. The effectiveness of the proposed model and method is validated via testing on the HYDICE and San Diego datasets. The denoising metric results of the PSNR, SSIM, and SAM are 41.79, 0.91, and 4.350 and 42.83, 0.93, and 3.558 on the HYDICE and San Diego datasets, respectively. The anomaly detection ACU is 0.943 and 0.959, respectively. The proposed method outperforms the other algorithms, demonstrating that the reconstructed images using this method exhibited lower noise levels and more complete image information, and the JointNet model outperforms the mainstream HSI anomaly detection algorithms in both the quantitative evaluation and visual effect, showcasing its improved detection capabilities. Full article

Non-invasive continuous health monitoring has become feasible with the advancement of biosensors. While monitoring certain biomarkers such as heart rate or skin temperature are now at a certain maturity, monitoring molecular biomarkers is still challenging. Progress has been shown in sampling, measurement, and interpretation of data toward non-invasive molecular sensors that can be integrated into daily wearable items. Toward this goal, this paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. In controlled experiments, we have shown the variation of glucose concentration in water can be wirelessly detected by tracking the resonant frequency of the embroidered sensors. The current sensors operate at 1.8GHz–2GHz and respond to the change in glucose concentration with a sensitivity of $0.17$ MHz/(mg/dL). The embroidered IDT-based sensors with wireless sensing will be a new measurement modality for molecular wearable sensors. The establishment of a wireless sensing mechanism for embroidered IDT-based sensors will be followed by an investigation of sweat for molecular detection. This will require adding functionalities for sampling and interpretation of acquired data. We envisage the embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture. Full article

The clinical diagnosis of Parkinson’s disease (PD) has been the subject of medical robotics research. Currently, a hot research topic is how to accurately assess the severity of Parkinson’s disease patients and enable medical robots to better assist patients in the rehabilitation process. The walking task on the Unified Parkinson’s Disease Rating Scale (UPDRS) is a well-established diagnostic criterion for PD patients. However, the clinical diagnosis of PD is determined based on the clinical experience of neurologists, which is subjective and inaccurate. Therefore, in this study, an automated diagnostic method for PD based on an improved multiclass support vector machine (MCSVM) is proposed in which wearable sensors are used. Kinematic analysis was performed to extract gait features, both spatiotemporal and kinematic, from the installed IMU and pressure sensors. Comparison experiments of three different kernel functions and linear trajectory experiments were designed. The experimental results show that the accuracies of the three kernel functions of the proposed improved MCSVM are 92.43%, 93.45%, and 95.35%. The simulation trajectories of the MCSVM are the closest to the real trajectories, which shows that the technique performs better in the clinical diagnosis of PD. Full article

Electronic textiles (e-textiles) merge textiles and electronics to monitor physiological and environmental changes. Innovations in textile functionalities and diverse applications have propelled e-textiles’ popularity. However, challenges like connection with external devices for signal processing and reliable interconnections between flexible textiles and rigid electronic circuits persist. Wearable connectors enable the effective communication of e-textiles with external devices. Factors such as electrical functionality and mechanical durability along with textile compatibility are crucial for their performance. Merging the rigid connectors on the flexible textiles requires conductive and flexible interconnects that can bridge this gap between soft and hard components. This work focuses on designing two-part detachable mechanical snap connectors for e-textiles. The textile side connectors are attached to the data transmission cables within the textiles using three interconnection techniques—conductive epoxy, conductive stitches, and soldering. Three types of connectors were developed that require three detaching or unmating forces (low, medium, and high). All connectors were subjected to 5000 mating–unmating cycles to evaluate their mechanical durability and electrical performance. Connectors with low and medium unmating forces exhibited a stable performance, while those with high unmating forces failed due to wear and tear. Conductive stitches maintained better conductance as compared to conductive epoxy and soldering methods. Full article

Anterior cruciate ligament (ACL) injuries are common in sports involving jumping and rapid direction changes, often occurring in non-contact situations. The risk of ACL injury is evaluated by knee flexion and valgus angles; a small knee flexion angle combined with a large valgus angle increases the risk. Monitoring these angles during activities can help athletes recognize their ACL injury risk and adjust their movements. Traditional 3D motion analysis, used for measuring knee angles, is costly and impractical for daily practice. This study proposes a knee supporter with stretch sensors to estimate knee flexion and valgus angles in practice settings, evaluating ACL injury risk and notifying athletes of high-risk movements. The proposed device wirelessly transmits data from three stretch sensors placed on the device to a PC and uses machine learning to estimate the knee angles. The results of the evaluation experiments, conducted with data from five healthy male and female participants in their twenties, indicate that the estimation accuracy for the knee flexion angle, achieved by a model trained using a Random Forest Regressor (RFR) with data from individuals other than the target user, resulted in a Mean Absolute Error (MAE) of 8.86 degrees. For the knee valgus angle, a model trained with the user’s own data using the RFR achieved a MAE of 0.81 degrees. Full article

Electronic textiles (e-textiles) are a branch of wearable technology based on integrating smart systems into textile materials creating different possibilities, transforming industries, and improving individuals’ quality of life. E-textiles hold vast potential, particularly for use in personal protective equipment (PPE) by embedding sensors and smart technologies into garments, thus significantly enhancing safety and performance. Although this branch of research has been active for several decades now, only a few products have made it to the market. Achieving durability, reliability, user acceptance, sustainability, and integration into current manufacturing processes remains challenging. High levels of reliability and user acceptance are critical for technical textiles, such as those used in PPE. While studies address washing reliability and field tests, they often overlook end user preferences regarding smart textiles. This paper presents a narrow fabric-based e-textile system co-developed by engineers, garment and textiles’ manufacturers, and firefighters. It highlights material choices and integration methods, and evaluates the system’s reliability, sustainability, and user experience, providing comprehensive insights into developing and analyzing e-textile products, particularly in the PPE field. Full article

Issues of size and power consumption in IoT devices can be addressed through triboelectricity-driven energy harvesting technology, which generates electrical signals without external power sources or batteries. This technology significantly reduces the complexity of devices, enhances installation flexibility, and minimizes power consumption. By utilizing shear thickening fluid (STF), which exhibits variable viscosity upon external impact, the sensitivity of triboelectric nanogenerator (TENG)-based sensors can be adjusted. For this study, the highest electrical outputs of STF and sponge-hybrid TENG (SSH-TENG) devices under various input forces and frequencies were generated with an open-circuit voltage (V_OC) of 98 V and a short-circuit current (I_SC) of 4.5 µA. The maximum power density was confirmed to be 0.853 mW/m² at a load resistance of 30 MΩ. Additionally, a lying state detection system for use in medical settings was implemented using SSH-TENG as a hybrid triboelectric motion sensor (HTMS). Each unit of a 3 × 2 HTMS array, connected to a half-wave rectifier and 1 MΩ parallel resistor, was interfaced with an MCU. Real-time detection of the patient’s condition through the HTMS array could enable the early identification of hazardous situations and alerts. The proposed HTMS continuously monitors the patient’s movements, promptly identifying areas prone to pressure ulcers, thus effectively contributing to pressure ulcer prevention. Full article

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user’s health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability. Full article

The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the naïve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality. Full article

Non-invasive monitoring of pulmonary health may be useful for tracking several conditions such as COVID-19 recovery and the progression of pulmonary edema. Some proposed methods use impedance-based technologies to non-invasively measure the thorax impedance as a function of respiration but face challenges that limit the feasibility, accuracy, and practicality of tracking daily changes. In our prior work, we demonstrated a novel approach to monitor respiration by measuring changes in impedance from the back of the thigh. We reported the concept of using thigh–thigh bioimpedance measurements for measuring the respiration rate and demonstrated a linear relationship between the thigh–thigh bioimpedance and lung tidal volume. Here, we investigate the variability in thigh–thigh impedance measurements to further understand the feasibility of the technique for detecting a change in the respiratory status due to disease onset or recovery if used for long-term in-home monitoring. Multiple within-session and day-to-day impedance measurements were collected at 80 kHz using dry electrodes (thigh) and wet electrodes (thorax) across the five healthy subjects, along with simultaneous gold standard spirometer measurements for three consecutive days. The peak–peak bioimpedance measurements were found to be highly correlated (0.94 ± 0.03 for dry electrodes across thigh; 0.92 ± 0.07 for wet electrodes across thorax) with the peak–peak spirometer tidal volume. The data across five subjects indicate that the day-to-day variability in the relationship between impedance and volume for thigh–thigh measurements is smaller (average of 14%) than for the thorax (40%). However, it is affected by food and water and might limit the accuracy of the respiratory tidal volume. Full article

Recovery and utilization of low-grade thermal energy is a topic of universal importance in today’s society. Photothermal conversion materials can convert light energy into heat energy, which can now be used in cancer treatment, seawater purification, etc., while thermoelectric materials can convert heat energy into electricity, which can now be used in flexible electronics, localized cooling, and sensors. Photothermoelectrics based on the photothermal effect and the Seebeck effect provide suitable solutions for the development of clean energy and energy harvesting. The aim of this paper is to provide an overview of recent developments in photothermal, thermoelectric, and, most importantly, photothermal–thermoelectric coupling materials. First, the research progress and applications of photothermal and thermoelectric materials are introduced, respectively. After that, the classification of different application areas of materials coupling photothermal effect with thermoelectric effect, such as sensors, thermoelectric batteries, wearable devices, and multi-effect devices, is reviewed. Meanwhile, the potential applications and challenges to be overcome for future development are presented, which are of great reference value in waste heat recovery as well as solar energy resource utilization and are of great significance for the sustainable development of society. Finally, the challenges of photothermoelectric materials as well as their future development are summarized. Full article

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