Search Results (191)

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

This paper presents a methodology for extracting and simulating arterial pulse waveform signals from Fabry–Perot interferometric measurements, emphasizing a practical approach for noninvasive cardiovascular assessment. A key novelty of this work is the presentation of a complete Python-based processing pipeline, which is made publicly available as open-source code on GitHub (git version 2.39.5). To the authors’ knowledge, no such repository for demodulating these specific interferometric signals to obtain a raw arterial pulse waveform previously existed. The proposed system utilizes accessible Python-based preprocessing steps, including outlier removal, Butterworth high-pass filtering, and min–max normalization, designed for robust signal quality even in settings with common physiological artifacts. Key features such as the rate of change, the Hilbert transform of the rate of change (envelope), and detected extrema guide the signal reconstruction, offering a computationally efficient pathway to reveal its periodic and phase-dependent dynamics. Visual analyses highlight amplitude variations and residual noise sources, primarily attributed to sensor bandwidth limitations and interpolation methods, considerations critical for real-world deployment. Despite these practical challenges, the reconstructed arterial pulse waveform signals provide valuable insights into arterial motion, with the methodology’s performance validated on measurements from three subjects against synchronized ECG recordings. This demonstrates the viability of Fabry–Perot sensors as a potentially cost-effective and readily implementable tool for noninvasive cardiovascular diagnostics. The results underscore the importance of precise yet practical signal processing techniques and pave the way for further improvements in interferometric sensing, bio-signal analysis, and their translation into clinical practice. Full article

Rotator cuff tears are common in individuals over 40, and physical therapy is often prescribed post-surgery. However, access can be limited by cost, convenience, and insurance coverage. CuffLink is a telehealth rehabilitation system that integrates the Strengthening and Stabilization System mechanical exerciser with the interACTION mobile health platform. The system includes a triple-axis accelerometer (LSM6DSOX + LIS3MDL FeatherWing), a rotary encoder, a VL530X time-of-flight sensor, and two wearable BioMech Health IMUs to capture upper-limb motion. CuffLink is designed to facilitate controlled, home-based exercise while enabling clinicians to remotely monitor joint function. Concurrent validity and test–retest reliability were used to assess device accuracy and repeatability. The results showed moderate to good validity for shoulder rotation (ICC = 0.81), device rotation (ICC = 0.94), and linear tracking (from zero: ICC = 0.75 and RMSE = 2.41; from start: ICC = 0.88 and RMSE = 2.02) and good reliability (e.g., RMSEs as low as 1.66 cm), with greater consistency in linear tracking compared to angular measures. Shoulder rotation and abduction exhibited higher variability in both validity and reliability measures. Future improvements will focus on manufacturability, signal stability, and force sensing. CuffLink supports accessible, data-driven rehabilitation and holds promise for advancing digital health in orthopedic recovery. Full article

High-performance flexible sensors capable of direct integration with biological tissues are essential for personalized health monitoring, assistive rehabilitation, and human–machine interaction. However, conventional devices face significant challenges in achieving conformal integration with biological surfaces, along with sufficient biomechanical compatibility and biocompatibility. This research presents an in situ 3D biomanufacturing strategy utilizing Direct Ink Writing (DIW) technology to fabricate functional bioelectronic interfaces directly onto human skin, based on a novel annealing PEDOT:PSS/PVA composite bio-ink. Central to this strategy is the utilization of a novel annealing PEDOT:PSS/PVA composite material, subjected to specialized processing involving freeze-drying and subsequent thermal annealing, which is then formulated into a DIW ink exhibiting excellent printability. Owing to the enhanced network structure resulting from this unique fabrication process, films derived from this composite material exhibit favorable electrical conductivity (ca. 6 S/m in the dry state and 2 S/m when swollen) and excellent mechanical stretchability (maximum strain reaching 170%). The material also demonstrates good adhesion to biological interfaces and high-fidelity printability. Devices fabricated using this material achieved good conformal integration onto a finger joint and demonstrated strain-sensitive, repeatable responses during joint flexion and extension, capable of effectively transducing local strain into real-time electrical resistance signals. This study validates the feasibility of using the DIW biomanufacturing technique with this novel material for the direct on-body fabrication of functional sensors. It offers new material and manufacturing paradigms for developing highly customized and seamlessly integrated bioelectronic devices. Full article

The surface profile and structural alignment of fibers and yarns in fabrics are critical factors affecting the electrical properties of conductive textile surfaces. This study aimed to investigate the impact of fabric surface roughness and the geometrical parameters of woven fabrics on their electrical resistance properties. Surface roughness was assessed using the MicroSpy^® Profile profilometer FRT (Fries Research & Technology) Metrology™, while electrical resistance was evaluated using the Van der Pauw method. The findings indicate that rougher fabric surfaces exhibit higher electrical resistance due to surface irregularities and lower yarn compactness. In contrast, smoother fabrics improve conductivity by enhancing surface uniformity and yarn contact. Fabric density, particularly weft density, governs the structural alignment of yarns. A 35% increase in weft density (W19–W27) resulted in a 13–15% reduction in resistance, confirming that denser fabrics facilitate current flow. Higher weft density also increases directional resistance differences, enhancing anisotropic behavior. Angular distribution analysis showed lower resistance and greater anisotropy at perpendicular orientations (0° and 180°, the weft direction; 90° and 270°, the warp direction), while diagonal directions (45°, 135°, 225°, and 315°) exhibited higher resistance. Surface roughness further hindered current flow, whereas increased weft density and surface mass reduced resistance and improved the directional dependencies of the electrical resistances. This analysis was conducted based on research using woven fabrics produced from silver-plated polyamide yarns (Shieldex^® 117/17 HCB). These insights support the optimization of these conductive fabrics for smart textiles, wearable sensors, and e-textiles. Fabric variants W19 and W21, with lower resistance variability and better isotropic behavior under the S electrode arrangement, could be proposed as suitable materials for integration into compact sensing systems like heart rate or bio-signal monitors. Full article

Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters for inland waters. Effective water quality parameter retrieval via optical satellite remote sensing requires three key components: (1) a sensor whose measurements are sensitive to variations in water quality; (2) accurate atmospheric correction to eliminate the effect of absorption and scattering in the atmosphere and retrieve the water-leaving radiance/reflectance; and (3) a bio-optical model used to estimate water quality from the optical signal. This study provides a literature review and an evaluation of these three components. First, a review of decommissioned, active, and upcoming satellite sensors is presented, highlighting their advantages and limitations, and a ranking method is introduced to assess their suitability for retrieving chlorophyll-a, colored dissolved organic matter, and non-algal particles in inland waters. This ranking can aid in selecting appropriate sensors for future studies. Second, the strengths and weaknesses of atmospheric correction algorithms used over inland waters are examined. The results show that no atmospheric correction algorithm performed consistently across all conditions. However, understanding their strengths and weaknesses allows users to select the most suitable algorithm for a specific use case. Third, the challenges, limitations, and recent advances of machine learning use in bio-optical models for inland water quality parameter retrieval are discussed. Machine learning models have limitations, including low generalizability, low dimensionality, spatial/temporal autocorrelation, and information leakage. These issues highlight the importance of locally trained models, rigorous cross-validation methods, and integrating auxiliary data to enhance dimensionality. Finally, recommendations for promising research directions are provided. Full article

The rapid growth of wearable technology has opened new possibilities for smart health-monitoring systems. Among various sensing methods, bio-impedance sensing has stood out as a powerful, non-invasive, and energy-efficient way to track physiological changes and gather important health information. This review looks at the basic principles behind bio-impedance sensing, how it is being built into wearable devices, and its use in healthcare and everyday wellness tracking. We examine recent progress in sensor design, signal processing, and machine learning, and show how these developments are making real-time health monitoring more effective. While bio-impedance systems offer many advantages, they also face challenges, particularly when it comes to making devices smaller, reducing power use, and improving the accuracy of collected data. One key issue is that analyzing bio-impedance signals often relies on complex digital signal processing, which can be both computationally heavy and energy-hungry. To address this, researchers are exploring the use of neuromorphic processors—hardware inspired by the way the human brain works. These processors use spiking neural networks (SNNs) and event-driven designs to process signals more efficiently, allowing bio-impedance sensors to pick up subtle physiological changes while using far less power. This not only extends battery life but also brings us closer to practical, long-lasting health-monitoring solutions. In this paper, we aim to connect recent engineering advances with real-world applications, highlighting how bio-impedance sensing could shape the next generation of intelligent wearable devices. Full article

Electrochemical biosensors are integrated bio-receptor–transducer devices that convert specific biological interactions into measurable electrical signals. Over the past decade, the use of novel nanomaterials, advanced enzyme immobilization techniques, and enhanced sensor architectures have been extensively studied, yielding significant progress in the design of highly sensitive, rapid, and reliable electrochemical biosensors. In the modern food industry various types of electrochemical biosensors are used, playing essential roles in the processes monitoring and optimization. This review highlights the strategies implemented to improve the analytical performance of electrochemical enzyme biosensors based on xanthine oxidase (XOx) for the quantitative detection of xanthine (X) and hypoxanthine (Hx), analytes relevant to the field of food quality control. The article covers recent developments (mainly original studies reported from 2010 to date) in the substrate materials, different electrode designs, working principles, advantages, limitations, and applications of XOx biosensors for meat freshness assessment. The article is meant to be a valuable resource that provides insights for improving design for the next generation bio-electroanalytical platforms to ensure food safety. Full article

The evaluation of driver workload in the lead vehicle of a driver-following autonomous truck platoon was conducted using bio-signal analysis. In this study, a single driver operated the lead vehicle while the second and third trucks followed autonomously. Three professional truck drivers (38 ± 4 years old, male) participated in the experiment. During driving, wearable sensors measured heart-rate variability indices, body acceleration, and skin temperature. The heart rate and body acceleration were sampled at 128 Hz (7.8 ms intervals), while skin temperature was recorded at 1 Hz. Each participant underwent three measurement sessions on different days, with each session lasting approximately 30–40 min. Statistical analysis was performed using repeated-measures ANOVA to determine significant differences across conditions and days. The results indicated that compared to solo driving, driving the lead vehicle of the autonomous platoon significantly increased skin temperature (p < 0.001), suggesting a higher physiological workload. This study provides insight into the physiological impact of autonomous platooning on lead-vehicle drivers, which is crucial for developing strategies to mitigate driver workload in such systems. Full article

Monitoring heart rate (HR) is vital for health management and athletic performance, and wearable technology enables scientists to obtain real-time cardiovascular insights. This study compares Machine Learning (ML) techniques, including Long Short-Term Memory (LSTM) networks, Physics-Informed Neural Networks (PINNs), and 1D Convolutional Neural Networks (1D CNNs). Then, we develop a hybrid Singular Spectrum Analysis (SSA)-Augmented ML technique to predict HR using wearable sensor data. Additionally, we investigate the impact of incorporating auxiliary physiological inputs, such as breathing rate (BR) and RR intervals, on predictive accuracy. The study utilizes the cardiorespiratory data acquired through wearable sensors while practising sports, including 126 recordings from 81 participants (53 males, 28 females) engaged in 10 different sports. Physiological signals were collected at 1 Hz using the BioHarness 3.0 (Zephyr Technology, Mangaluru, India). The dataset includes individuals with varied levels of sports experience (beginner, intermediate, and advanced), allowing for a more comprehensive evaluation of HR variability across different expertise levels. Our results demonstrate that the hybrid SSA-LSTM model reaches the lowest prediction error by effectively capturing HR dynamics. Furthermore, integrating HR, BR, and RR data significantly enhances accuracy over single or dual parameter inputs. These findings support adopting multivariate machine learning models for health monitoring, improving HR prediction accuracy for fitness and preventive healthcare. Full article

Neurological diseases leading to motor deficits constitute significant challenges to healthcare systems. Despite technological advancements in data acquisition, sensor development, data processing, and virtual reality (VR), a suitable framework for patient-centered neuromotor robot-assisted rehabilitation using collective sensor information does not exist. An extensive literature review was achieved based on 124 scientific publications regarding different types of sensors and the usage of the bio-signals they measure for neuromotor robot-assisted rehabilitation. A comprehensive classification of sensors was proposed, distinguishing between specific and non-specific parameters. The classification criteria address essential factors such as the type of sensors, the data they measure, their usability, ergonomics, and their overall impact on personalized treatment. In addition, a framework designed to collect and utilize relevant data for the optimal rehabilitation process efficiently is proposed. The proposed classifications aim to identify a set of key variables that can be used as a building block for a dynamic framework tailored for personalized treatments, thereby enhancing the effectiveness of patient-centered procedures in rehabilitation. Full article

The operating principle of a fuel cell is attracting increasing attention in the development of self-powered electrochemical sensors (SPESs). In this type of sensor, the chemical energy of the analyzed substance is converted into electrical energy in a galvanic cell through spontaneous electrochemical reactions, directly generating an analytical signal. Unlike conventional (amperometric, voltammetric, and impedimetric) sensors, no external energy in the form of an applied potential is required for the redox detection reactions to occur. SPESs therefore have several important advantages over conventional electrochemical sensors. They do not require a power supply and modulation system, which saves energy and costs. The devices also offer greater simplicity and are therefore more compatible for applications in wearable sensor devices as well as in vivo and in situ use. Due to the dual redox properties of hydrogen peroxide, it is possible to develop membraneless fuel cells and fuel-cell-based hydrogen peroxide SPESs, in which hydrogen peroxide in the analyzed sample is used as the only source of energy, as both an oxidant and a reductant (fuel). This also suppresses the dependence of the devices on the availability of oxygen. Electrode catalyst materials for different hydrogen peroxide reaction pathways at the cathode and the anode in a one-compartment cell are a key technology for the implementation and characteristics of hydrogen peroxide SPESs. This article provides an overview of the operating principle and designs of H₂O₂–H₂O₂ fuel cells and H₂O₂ fuel-cell-based SPESs, focusing on biomimetic and nanozyme catalysts, and highlights recent innovations and prospects of hydrogen-peroxide-based SPESs for (bio)electrochemical analysis. Full article

This paper presents the architecture, design, and testing results of a scalable, multi-core, multi-function sensor interface, integrating CMOS technology and memristor elements for efficient neuromorphic and bio-inspired analysis. The architecture leverages the high-density and non-volatile properties of memristors to support different analysis functions. Each processing core is equipped with hybrid CMOS/memristor arrays, enabling real-time parallel acquisition and analysis, and each can be configured independently. The system facilitates communication between cores and is fully scalable. The first implementation supports 16 input channels, storing 256 neural signal samples, and the second implementation supports 576 input channels, storing 9k neural signal samples. Full article

In the growing field of personalized medicine, non-invasive wearable devices and sensors are valuable diagnostic tools for the real-time monitoring of physiological and biokinetic signals. Among all the possible multiple (bio)-entities, pH is important in defining health-related biological information, since its variations or alterations can be considered the cause or the effect of disease and disfunction within a biological system. In this work, an innovative (bio)-electrochemical flexible pH sensor was proposed by realizing three electrodes (working, reference, and counter) directly on a polyimide (Kapton) sheet through the implementation of CO₂ laser writing, which locally converts the polymeric sheet into a laser-induced graphene material (LIG electrodes), preserving inherent mechanical flexibility of Kapton. A uniform distribution of nanostructured PEDOT:PSS was deposited via ultrasonic spray coating onto an LIG working electrode as the active material for pH sensing. With a pH-sensitive PEDOT coating, this flexible sensor showed good sensitivity defined through a linear Nernstian slope of (75.6 ± 9.1) mV/pH, across a pH range from 1 to 7. We demonstrated the capability to use this flexible pH sensor during dynamic experiments, and thus concluded that this device was suitable to guarantee an immediate response and good repeatability by measuring the same OCP values in correspondence with the same pH applied. Full article

A detailed analysis of the inertial signals input is required when using deep learning models for Parkinson’s Disease detection. This work explores the possibility of reducing the input size of the models by studying the most appropriate frequency range and determines if it is feasible to evaluate subjects with different sensor locations than those used during training. For experimentation, 3.2 s windows are used to classify signals between Parkinson’s patients and control subjects, applying Fast Fourier Transform to the inertial signals and following a Leave-One-Subject-Out Cross-Validation methodology for the PD-BioStampRC21 dataset. It has been observed that the frequency range of 0 to 5 Hz offers a classification accuracy rate of 75.75 ± 0.62% using the five available sensors for training and evaluation, which is close to the model’s performance over the entire frequency range, from 0 to 15.625 Hz, which is 77.46 ± 0.60%. Regarding the transfer learning between sensors located in different body parts, it was observed that training and evaluating the model using data from the right forearm resulted in an accuracy of 65.17 ± 0.69%. When the model was trained with data from the opposite forearm, the accuracy was similar, at 63.57 ± 0.69%. Likewise, comparable results were found when using data from the other forearm and when training and evaluating with opposite thighs, with accuracy reductions not exceeding 3%. Full article