Search Results (796)

With the rapid technological advancements in wearable devices, motion and health management have significantly improved, enabling the measurement of various biometric data with compact equipment. Our research focuses on motion measurement but, in general, full-body motion estimation requires motion capture systems or multiple inertial sensors, making it necessary to directly measure movement itself. In this study, we propose estimating full-body posture using inverse kinematics based on trunk posture and limb-end information collected through wearable devices. To enhance estimation accuracy in this underdetermined problem, we employ Physics-Informed Neural Networks (PINNs), which efficiently learn using physical laws as a loss function, along with a high-precision inverse kinematics model of a digital human. Through this approach, we enable high-accuracy full-body posture estimation even with wearable devices in underdetermined scenarios. Full article

Despite significant advancements in indoor navigation technology over recent decades, it still faces challenges due to excessive dependency on external infrastructure and unreliable positioning in complex environments. This paper proposes an autonomous localization system that integrates advanced adaptive pedestrian dead reckoning (APDR) and binocular vision, designed to provide a low-cost, high-reliability, and high-precision solution for rescuers. By analyzing the characteristics of measurement data from various body parts, the chest is identified as the optimal placement for sensors. A chest-mounted advanced APDR method based on dynamic step segmentation detection and adaptive step length estimation has been developed. Furthermore, step length features are innovatively integrated into the visual tracking algorithm to constrain errors. Visual data is fused with dead reckoning data through an extended Kalman filter (EKF), which notably enhances the reliability and accuracy of the positioning system. A wearable autonomous localization vest system was designed and tested in indoor corridors, underground parking lots, and tunnel environments. Results show that the system decreases the average positioning error by 45.14% and endpoint error by 38.6% when compared to visual–inertial odometry (VIO). This low-cost, wearable solution effectively meets the autonomous positioning needs of rescuers in disaster scenarios. Full article

Wearable sensors are central to health-monitoring systems, but the limited capacity of compact batteries poses a challenge for long-term and maintenance-free operation. In this study, we investigated ambient electromagnetic wave (AEMW) energy harvesting using a human body antenna (HBA) as a means to supply power to wearable sensors. The power density and frequency distribution of AEMWs were measured in diverse indoor, outdoor, and basement environments. We designed and fabricated a flexible HBA–circuit interface electrode, optimized for broadband impedance matching when worn on the body. Experimental comparisons using a simulated AEMW source demonstrated that the HBA outperformed a conventional small whip antenna, particularly at frequencies below 300 MHz. Furthermore, the outdoor measurements indicated that the power harvested by the HBA was estimated to be −31.9 dBm (0.64 μW), which is sufficient for the intermittent operation of low-power wearable sensors and Bluetooth Low Energy modules. The electromagnetic safety was also evaluated through numerical analysis, and the specific absorption rate was confirmed to be well below the international safety limits. These findings indicate that HBA-based AEMW energy harvesting provides a practical and promising approach to achieving battery-maintenance-free wearable devices. Full article

Nafion has long been recognized as the gold standard for proton exchange membranes, due to its exceptional ion exchange capacity and its advanced performance in chemically aggressive environments. In recent years, a growing body of evidence has demonstrated that Nafion is equally well-suited in complex biological conditions owing to its structural robustness, responsive functionality and intrinsic biocompatibility. These characteristics have enabled its transition into the biomedical and healthcare sectors, where it is currently being explored for a diverse and expanding range of applications. To that end, Nafion has been systematically investigated as a key component in bioelectronic systems for energy harvest, sensors, wearable electronics, tissue engineering, lab-on-a-chip platforms, implants, controlled drug delivery systems and antimicrobial surface coatings. This review examines the distinctive structural and electrochemical characteristics that underpin Nafion’s performance in these biomedical contexts, provides an overview of recent advancements, emphasizes critical performance metrics and highlights the material’s growing potential to shape the future of biomedical technology. Full article

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

Recent pressure sensor research often focuses on developing sensors for impulse applications, including touch sensors, e-skin development, or physiological monitoring. However, static loading applications, such as those needed for compression garment design, are significantly under-researched in comparison. Many technology solutions do not translate across applications, as static loading requires measurements which have high accuracy, high precision, and low drift. To address the gap in sensor development between impulse and static applications, we define a literature-based taxonomy providing two conceptual classifications based on sensor functionality and specific design characteristics. The taxonomy’s utility is demonstrated through the mapping of sensors onto compression garment development phases by matching application requirements with sensor performance. The taxonomy developed will advance research and the industry by providing a roadmap of how sensor characteristics influence performance to drive a focused development for future sensors, specifically for compression garment innovation. Full article

Diabetic foot ulcers (DFUs) represent a critical global health issue, necessitating the development of advanced smart, flexible, and wearable sensors for continuous monitoring that are reimbursable within foot orthotics. This study presents the design and characterization of a pressure sensor implemented into a shoe insole to monitor diabetic wound pressures, emphasizing the need for a high sensitivity, durability under cyclic mechanical loading, and a rapid response time. This investigation focuses on the electrical and mechanical properties of carbon nanotube (CNT) composites utilizing Ecoflex and polydimethylsiloxane (PDMS). Morphological characterization was conducted using Transmission Electron Microscopy (TEM), Laser Confocal Microscopy, and Scanning Electron Microscopy (SEM). The electrical and mechanical properties of the CNT/Ecoflex- and the CNT/PDMS-based sensor composites were then investigated. CNT/Ecoflex was then further evaluated due to its lower variability performance between cycles at the same pressure, as well as its consistently higher capacitance values across all trials in comparison to CNT/PDMS. The CNT/Ecoflex composite sensor showed a high sensitivity (2.38 to 3.40 kPa⁻¹) over a pressure sensing range of 0 to 68.95 kPa. The sensor’s stability was further assessed under applied pressures simulating human weight. A custom insole prototype, incorporating 12 CNT/Ecoflex elastomeric matrix-based sensors (as an example) distributed across the metatarsal heads, midfoot, and heel regions, was developed and characterized. Capacitance measurements, ranging from 0.25 pF to 60 pF, were obtained across N = 3 feasibility trials, demonstrating the sensor’s response to varying pressure conditions linked to different body weights. These results highlight the potential of this flexible insole prototype for precise and real-time plantar surface monitoring, offering an approachable avenue for a challenging diabetic orthotics application. Full article

This study evaluates the accuracy of a low-cost wearable system for the continuous monitoring of vital signs, including heart rate, blood oxygen saturation (Sp${\mathrm{O}}_2$), blood pressure trend (BPT), and body temperature. The prototype was built using the nRF52840 microcontroller, which integrates photoplethysmography and infrared sensors. The heart rate and Sp${\mathrm{O}}_2$ data were collected under three body positions (Rest, Sitting, and Standing), while all measurements were performed using both anatomical configurations: BPT-Finger and BPT-Earlobe. Results were compared against validated commercial devices: UT-100 for heart rate and Sp${\mathrm{O}}_2$, G-TECH LA800 for blood pressure, and G-TECH THGTSC3 for body temperature. Ten participants were monitored over a ten-day period. Bland–Altman analysis revealed clinically acceptable agreement thresholds of ±5 mmHg for blood pressure, ±5–10 bpm for heart rate, ±4% for Sp${\mathrm{O}}_2$, and ±0.5 °C for temperature. Both wearable configurations demonstrated clinically acceptable agreement across all vital signs. The BPT-Earlobe configuration exhibited superior stability and lower variability in the Rest and Sitting positions, likely due to reduced motion artifacts. Conversely, the BPT-Finger configuration showed higher Sp${\mathrm{O}}_2$ accuracy in the Standing position, with narrower limits of agreement. These findings highlight the importance of sensor placement in maintaining measurement consistency across physiological conditions. With an estimated cost of only ~USD 130—compared to ~USD 590 for the commercial alternatives—the proposed system presents a cost-effective, scalable, and accessible solution for decentralized health monitoring, particularly in underserved or remote environments. Full article

The development of materials science, artificial intelligence and wearable technology has created both opportunities and challenges for the next generation of bionic sensor technology. Bionic sensors are extensively utilized in the collection and monitoring of human biological signals. Human biological signals refer to the parameters generated inside or outside the human body to transmit information. In a broad sense, they include bioelectrical signals, biomechanical information, biomolecules, and chemical molecules. This paper systematically reviews recent advances in bionic sensors in the field of biometric acquisition and monitoring, focusing on four major technical directions: bioelectric signal sensors (electrocardiograph (ECG), electroencephalograph (EEG), electromyography (EMG)), biomarker sensors (small molecules, large molecules, and complex-state biomarkers), biomechanical sensors, and multimodal integrated sensors. These breakthroughs have driven innovations in medical diagnosis, human–computer interaction, wearable devices, and other fields. This article provides an overview of the above biomimetic sensors and outlines the future development trends in this field. Full article

Object-related accidents among older adults often result from inadequately designed furniture and fixtures that do not accommodate age-related changes. However, technologies for quantitatively capturing how furniture and fixtures assist the body in daily life remain limited. This study addresses this gap by introducing a portable, non-disruptive system that measures and visualizes how humans interact with environmental objects, particularly during transitional movements such as standing, turning, or reaching. The system integrates wearable force sensors, motion capture gloves, RGB-D cameras, and LiDAR-based environmental scanning to generate spatial maps of body-applied forces, overlaid onto point cloud representations of actual living environments. Through home-based experiments involving 13 older adults aged 69–86 across nine households, the system effectively identified object-specific support interactions with specific furniture (e.g., doorframes, shelves) and enabled a three-dimensional comparative analysis across different spaces, including living rooms, entryways, and bedrooms. The visualization captured essential spatial features—such as contact height and positional context—without altering the existing environment. This study presents a novel methodology for evaluating life environments from a life-centric perspective and offers insights for the inclusive design of everyday objects and spaces to support safe and independent aging in place. Full article

This paper presents a comparative study for detecting the activities of daily living (ADLs) using two distinct sensor systems: the FlexTail wearable spine tracker and a camera-based pose estimation model. We developed a protocol to simultaneously record data with both systems and capture eleven activities from general movement, household, and food handling. We tested a comprehensive selection of state-of-the-art time series classification algorithms. Both systems achieved high classification performance, with average $F_1$ scores of 0.90 for both datasets using a 1-second time window and the random dilated shapelet transform (RDST) and QUANT classifier for FlexTail and camera data, respectively. We also explored the impact of hierarchical activity grouping and found that while it improved classification performance in some cases, the benefits were not consistent across all activities. Our findings suggest that both sensor systems recognize ADLs. The FlexTail model performs better for detecting sitting and transitions, like standing up, while the camera-based model is better for activities that involve arm and hand movements. Full article

Human activity recognition using wearable accelerometer data can be a useful digital biomarker for severity assessment and the diagnosis of diseases, where the relationship between onset and patient activity is crucial. For long-term monitoring in clinical settings, the volume of data collected over time should be minimized to reduce power consumption, computational load, and communication volume. This study aimed to determine the lowest sampling frequency that maintains recognition accuracy for each activity. Thirty healthy participants wore nine-axis accelerometer sensors at five body locations and performed nine activities. Machine-learning-based activity recognition was conducted using data sampled at 100, 50, 25, 20, 10, and 1 Hz. Data from the non-dominant wrist and chest, which have previously shown high recognition accuracy, were used. Reducing the sampling frequency to 10 Hz did not significantly affect the recognition accuracy for either location. However, lowering the frequency to 1 Hz decreases the accuracy of many activities, particularly brushing teeth. Using data with a 10 Hz sampling frequency can maintain recognition accuracy while decreasing data volume, enabling long-term patient monitoring and device miniaturization for clinical applications. Full article

Wearable tactile sensors with high sensitivity can be potentially used to continuously monitoring physiological signals that are closely related to disease diagnosis and health condition tracking. However, the development of such tactile sensors involves a number of challenges, including a series of expensive patterning processes for microstructure manufacturing and addressing the large thickness of the microstructured composite film. Herein, a mold-free approach is presented to develop an ultra-thin ZnO/PEDOT:PSS composite film with flower-like microstructures via a feasible solution process for highly sensitive tactile sensors. The fabricated tactile sensors exhibit a high sensitivity of 4 × 10³ kPa⁻¹ in the pressure range 0–10 kPa, a fast response to various pressures in merits of the hierarchical microstructures on top of the ultra-thin composite films. Thanks to the fascinating performance of the devices, the tactile sensors are demonstrated with the ability to monitor physiological signals, subtle human body motions, and spatial pressure distribution. Full article

Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless sensor node (WSN) that was developed within the Horizon Europe SOCIO-BEE project to support air quality monitoring through citizen science (CS). The low-cost, body-mounted WSN measures NO₂, O₃, and PM_2.5. Three pilot campaigns were conducted in Ancona (Italy), Maroussi (Greece), and Zaragoza (Spain), and involved diverse user groups—seniors, commuters, and students, respectively. PM_2.5 sensor data were validated through two approaches: direct comparison with reference stations and spatial clustering analysis using K-means. The results show strong correlation with official PM_2.5 data (R² = 0.75), with an average absolute error of 0.54 µg/m³ and a statistical confidence interval of ±3.3 µg/m³. In Maroussi and Zaragoza, where no reference stations were available, the clustering approach yielded low intra-cluster coefficients of variation (CV = 0.50 ± 0.40 in Maroussi, CV = 0.28 ± 0.30 in Zaragoza), indicating that the measurements had high internal consistency and spatial homogeneity. Beyond technical validation, user engagement and perceptions were evaluated through pre-/post-campaign surveys. Across all pilots, over 70% of participants reported satisfaction with the system’s usability and inclusiveness. The findings demonstrate that wearable low-cost sensors, when supported by a structured engagement and data validation framework, can provide reliable, actionable air quality data, empowering citizens and informing evidence-based environmental policy. Full article

With growing interest in wearable technologies, the development of flexible sensors and products that can monitor the human body while being comfortable to wear is gaining momentum. While various textile-based strain sensors have been proposed, their implementation in practical, exercise-specific applications remains limited. In this study, we developed a knitted strain sensor that monitors elbow angles, focusing on dumbbell exercise, which is a basic exercise in sports, and verified its performance. The material of the developed knitted strain sensor with a plain stitch structure comprised a silver-coated nylon conductive yarn and an acrylic/wool blended yarn. To evaluate the electrical and physical characteristics of the developed sensor, a textile folding tester was used to conduct 100 repeated bending experiments at three angles of 30°, 60°, 90° and speeds of 10, 30, 60 cpm. The system demonstrated excellent elasticity, high sensitivity (gauge factor = 698), fast responsiveness, and reliable performance under repeated stress, indicating its potential for integration into wearable fitness or rehabilitation platforms. Full article