Search Results (397)

Human presence detection is critical when building smart cities with use cases in sectors like smart homes, emergency evacuation, health-care monitoring and others. Existing human detection systems predominantly rely on camera-based imaging, raising privacy concerns. Moreover, conventional FMCW radar approaches are primarily motion-based, thus often failing to detect the presence of unconscious individuals, as in the case of search and rescue (SAR) operations. Some radar approaches use Doppler or spectral peak analysis to estimate respiration but fail to exploit phase coherence to resolve sub-millimeter chest displacement and higher-order physiological harmonics. This paper presents an interferometric radar framework that models multi-feature vital-sign signatures for human detection under controlled clinical settings using respiratory harmonic relationships, inter-harmonic consistency, chest-displacement spectral characteristics, and radar-derived cardiac mechanical signatures. Physiological relationships are used to establish the expected structure of the extracted features, while subject-to-subject variability and measurement uncertainty are used to determine practical acceptance regions from the training cohort. Experimental data from 30 healthy subjects were analyzed using a single interferometric radar sensor under controlled clinical conditions. The resulting signatures were subsequently evaluated using a machine-learning validation pipeline. With 243 test cases, the proposed framework achieved 89.71% accuracy, 95.26% precision, 94.15% F1-score, and 93.06% sensitivity. The study demonstrates that interferometric chest-displacement sensing can provide a privacy-preserving physiological feature space for human presence detection, while also identifying the limitations associated with unresolved multi-person signal superposition and hardware-induced phase uncertainty. Moreover, interferometric sensing by principle will work better than conventional radar approaches for SAR operations. Although validated in a controlled clinical environment, the framework establishes a foundational pathway towards future research for eventual deployment in next-generation smart systems. Full article

We present a dataset from a wearable sensor that monitors respiration, electrocardiography (one lead), body position/actigraphy, and skin temperature, suitable for sleep studies. We collected the dataset in parallel with standard in-laboratory polysomnography (PSG) and used it to validate ASPEN-derived metrics against clinical assessments. Data were collected from seven volunteers (mean age = 55.7 ± 12.5 years and body mass index (BMI) = 29.9 ± 3.78 kg/m²) for a total of 63 h during sleep studies at the Flinders University Sleep Lab. Our preliminary analysis shows broad agreement between standard metrics used to assess sleep quality measured from the wearable sensor and the resident (Compumedics) polysomnography system, such as respiratory rate, heart rate, and sleep posture. Therefore, we make this dataset of high-resolution ASPEN signals, along with synoptic participant data (age, gender, BMI), available to support the development and benchmarking of algorithms for wearable sleep monitoring. Full article

Soilless systems are increasingly important in protected horticulture because they improve water and nutrient-use efficiency, support year-round production, and reduce dependence on field conditions. However, the root zone is still commonly managed as a passive nutrient reservoir using mainly electrical conductivity and pH set points. This review presents the root zone as an actively engineered biological environment in which dissolved oxygen, root-zone temperature, nutrient-solution chemistry and hydraulics, microbiomes and biofilms, and sensor-guided control interact to determine crop performance. These domains converge on root respiration and ATP production, membrane transport, aquaporin activity, hydraulic conductance, calcium delivery, oxidative balance, and microbial or pathogen selection. Their combined effects influence fresh mass, tissue hydration, nutrient uptake, phytochemical composition, tipburn incidence, disease resilience, and overall system stability. Recent evidence indicates that active aeration, targeted root-zone heating or cooling, optimized flow scheduling, and calcium-focused interventions can improve the yield and quality of leafy vegetables, although responses vary with crop species, cultivar, developmental stage, and production-system architecture. Current evidence also indicates important uncertainties, including crop- and cultivar-specific response thresholds, architecture-dependent performance, energy and resource costs, and the still-limited predictability of microbiome manipulation. Emerging sensing, machine learning, digital-twin, and predictive-control approaches could enable a transition from threshold-based correction to physiology-informed root-zone state management. Nevertheless, wider commercial translation is constrained by inconsistent reporting of sensor location, hydraulic conditions, nutrient composition, microbial status, and resource use. We therefore propose a minimum reporting framework and research priorities for developing reproducible, energy-aware, microbiologically robust, and crop-specific root-zone management strategies for closed soilless horticulture. Full article

Flexible capacitive humidity sensors are pivotal for next-generation wearable electronics and Internet of Things (IoT) applications. However, conventional devices suffer from severe salt leaching and delamination of hygroscopic sensing materials, alongside poor interfacial adhesion and mechanical fragility of metallic electrodes. Herein, an innovative in situ strategy is reported for constructing LiCl-CH₃COOK/laser-induced graphene (LIG) composite flexible electrodes via single-step laser direct writing. This approach simultaneously patterns three-dimensional (3D) porous LIG interdigitated networks on polyimide substrates and drives deep infiltration of the LiCl-CH₃COOK hygroscopic phase within the graphene pores. The 3D interconnected LIG skeleton not only provides abundant physical anchoring sites and rapid water vapor transport channels but also effectively suppresses the physical loss and leaching of the deliquesced salts through micro-nanoscale spatial confinement, yielding remarkable interfacial stability and cycling lifetime. Benefiting from the synergistic deliquescence of the composite salts, the sensor delivers an exceptional sensitivity of 65,570% (ΔC/C₀), moderate response/recovery times of 75/90 s, and ultralow hysteresis of 0.981%. Furthermore, the streamlined laser-scribing route replaces conventional costly microfabrication sequences, enabling low-cost, high-precision customization. Demonstrations in human respiration monitoring and smart agriculture validate the sensor’s superior reliability and practical applicability, establishing a novel pathway for miniaturized, highly integrated, and robust flexible humidity detection systems. Full article

This paper presents an experimental framework for synchronizing multi-modal physiological data in a real-world driving environment. Research-grade sensors (CardioBAN, respiBAN) were integrated with consumer wearables (Huawei Watch D2, Oura, and Xmart smart rings) to monitor driver heart rate (HR) and respiration rate (RR). A custom MATLAB (version R2024a, 24.1.0)-based workflow was developed to align disparate data streams, using a nearest-neighbor principle to ensure temporal accuracy. The setup was validated through 60 min driving sessions, successfully correlating physiological responses with video feeds. Our results demonstrate that, with proper synchronization, consumer wearables can be compared with precise research-grade equipment for continuous driver state monitoring. Full article

Wide-scale, spatio-temporal quantification of soil CO₂ efflux is essential for understanding terrestrial carbon dynamics, predicting climate change, and evaluating the carbon balance in managed and natural ecosystems. Rising global temperatures, changing land use patterns, and other activities aimed at boosting crop productivity have resulted in an increase in microbial activity, increasing the impact of soil on gas exchange. Therefore, it is important to measure ${\mathrm{CO}}_2$ gas exchange in situ, over wide areas and extended periods without manual intervention. However, current approaches such as remote sensing lacks sufficient spatial and depth resolution, while other direct measurements such as eddy covariance demand expensive infrastructure, limiting wide-scale deployment. In this work, we propose a low-cost, battery-operated ${\mathrm{CO}}_2$ sensing system that provides long-term and scalable monitoring of soil respiration and carbon flux, with the promise for high-resolution measurements. Our innovative design features a PVC-based gas chamber that periodically opens and closes to allow for gas exchange, and a sensor module with low-cost temperature, moisture, pressure, and ${\mathrm{CO}}_2$ sensors, with a low-power wireless LoRa network for real-time monitoring. Our system was rigorously validated through multiple outdoor deployments, over long periods to demonstrate its practicality. We observe that temperature, air pressure, and humidity trends show responsiveness to the environment. We also observe that ${\mathrm{CO}}_2$ emission flux rate vary significantly across different modules, underscoring the need for fine-grained spatial and temporal resolution in monitoring. Full article

Barn-level monitoring may mask local heat-risk exposure in open-sided beef cattle barns. This study evaluated a long-range wireless multi-point monitoring framework for pen-scale heat-risk mapping in a commercial open-sided double-row barn during the hot season. Environmental data were continuously collected from representative front, middle, and rear pens, and the temperature–humidity index (THI) was matched with respiration rate, per-head daily water intake, and descriptive body-weight records. The data completeness of the main analytical nodes was 99.75–99.88%. Mixed-effects models showed that the middle and rear pens had higher daily and daytime THI intensity than the front pen (p < 0.001), whereas the daytime duration of THI ≥ 78 was similar among pens. The rear–front THI difference was amplified during 10:00–16:00 (β = 1.19, p = 0.006). Respiration rate increased by 1.25 breaths min⁻¹ for each one-unit increase in THI (95% CI: 0.92–1.57, p < 0.001). Per-head daily water intake was more strongly associated with daily mean THI than with daily maximum THI. These findings indicate that pen-scale monitoring can identify local thermal heterogeneity and provide a data basis for targeted environmental assessment in open-sided beef cattle barns. Full article

Flexible piezoresistive sensors often face a trade-off between sensitivity and working range. In this work, a gradient porous poly(vinyl alcohol)/carbon black (PVA/CB) composite was fabricated via a simple sugar-templating method. The bilayer structure consists of a small-pore layer and a large-pore layer, enabling sequential deformation under external pressure. As a result, the sensor exhibits a sensitivity of −3.05 kPa⁻¹ in the low-pressure range (0–20 kPa) and maintains a stable response up to 120 kPa. Compared with uniform porous structures, the gradient design shows improved performance in the medium- and high-pressure ranges. The sensor also demonstrates good repeatability, fast response, and stability over 1000 cycles. Practical applications including respiration monitoring, vocal vibration detection, and motion sensing are demonstrated. This work provides a simple and scalable approach for developing flexible pressure sensors. Full article

With the rapid advancement of wearable technologies, high-performance flexible sensors have garnered significant research interest. This study presents a PAM-5 hydrogel characterized by exceptional tensile strain (425%), superior compressive modulus (325 kPa), and notable ionic conductivity (1.1 S/m), serving as a robust mechanical framework and electrical foundation for developing advanced sensors. The PNP-5 integrated hydrogel sensor fabricated from this material demonstrates an extensive sensing range (2–53 kPa), remarkable sensitivity, and rapid response time (~321 ms), with its outstanding performance attributed to the synergistic structural design. Furthermore, the sensor exhibits excellent durability, maintaining consistent voltage output (~6.5 mV) across 1000 compression cycles, confirming its long-term operational stability. Through real-time monitoring of physiological signals and biomechanical movements including finger bending, respiration, and grasping, combined with spatial pressure mapping experiments using a 5 × 5 array touchpad, the device’s potential applications in wearable sensing platforms and human–machine interface systems are effectively demonstrated. This self-powered hydrogel sensor not only advances the performance metrics of flexible electronic devices but also establishes a solid experimental basis for future development of intelligent materials in health monitoring and interactive technologies. Full article

Real-time simultaneous monitoring of cardiac and respiratory signals in wearable systems remains challenging due to motion artifacts under varying body postures. This paper proposes an edge wearable integrated with novel methods to acquire and process cardiac and respiratory signals simultaneously from the chest. For respiration monitoring, the system captures chest motion using an IMU and applies dynamic filtering with real-time axis selection in a custom mobile application. It automatically selects the best respiratory axis from six IMU vectors: x, y, z, xy, yz, and zx, improving robustness across body postures and device placements. The proposed system can also capture and process electrocardiogram (ECG) data simultaneously for cardiac monitoring. Cardiac and respiration data were taken from eight healthy subjects, including males and females, followed by five protocols. The overall mean absolute error (MAE) for eight subjects is found to be 0.64 breath per minute (BrPM) after validation by a commercial sensor. In real time, the wearable continuously provides ECG and breathing signal streaming, ECG beat detection, and cardiac and breathing rates with an overall latency of 13.8 ms. The results indicate that the system can monitor the cardio-respiratory signals in real time under static and light-movement conditions. Full article

Computed tomography (CT) examinations pose challenges for continuous patient observation, particularly when adverse events such as severe reactions to contrast media occur. To improve patient monitoring in such situations, this preliminary study proposes a contactless method for respiratory waveform estimation using a depth camera and AI-based body detection. The method identifies anatomically relevant respiratory regions and extracts depth-based motion signals while subjects are seated facing the camera, which is positioned approximately 2 m away. Performance was evaluated experimentally using a wearable force-sensor respiration belt as the reference. Quantitative assessment was conducted using waveform error metrics, Pearson correlation coefficients, respiratory-rate agreement, and Bland–Altman analysis, while qualitative analysis was used to examine the influence of clothing conditions on measurement performance. The results show that the proposed method can provide stable respiratory waveform estimation, with the chest region yielding the lowest waveform error and the highest correlation among the evaluated ROIs. Bland–Altman analysis further indicated small systematic errors in respiratory-rate estimation, although variability-related indices were affected by ROI selection and clothing conditions. These findings support the feasibility of the proposed approach for contactless respiratory monitoring during CT examinations and indicate that the main contribution of this study is to clarify the importance of anatomical ROI selection for robust waveform extraction under CT-oriented monitoring conditions. Full article

Driver drowsiness detection has become an important application of sensor-based monitoring systems aimed at improving road safety. This review focuses on sensing technologies and physiological parameters used for real-time drowsiness detection in drivers. The surveyed approaches are categorized into physiological sensing methods, including electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and photoplethysmography (PPG), and mechanical sensing methods, including respiration rate, eye blinking, head movement, yawning, and steering wheel gripping force. Each method is analyzed from a sensor system perspective, considering signal acquisition principles, measurement location, and practical deployment constraints. In addition, the reviewed techniques are evaluated based on real-time capability, level of sensor attachment, cost, restriction of user movement, and suitability for standalone operation. The comparison highlights that mechanical sensing approaches provide non-invasive and cost-effective solutions; however, they are sensitive to environmental noise and behavioral variability. In contrast, physiological sensing methods offer more direct and earlier indicators of fatigue-related changes in biosignals, although they typically require wearable or contact-based sensors and more complex acquisition systems. The review further indicates that multimodal sensor fusion is increasingly being adopted to improve robustness and reliability in real-world driving conditions. Overall, this work provides a structured overview of sensing modalities and highlights key considerations for designing efficient, real-time driver monitoring systems. Full article

Advances in wearable device and sensor technologies progressively shift respiratory monitoring from the clinical setting to real-world conditions. This rapidly developing field allows for more accurate diagnostics. However, reliable monitoring during dynamic activities remains challenging due to artifacts caused by movement, postural changes, electrode drift, and variability in breathing patterns. Therefore, this review focuses on wearable methodologies capable of determining respiratory rate and potentially tidal volume during strenuous physical activities. Direct sensing approaches, including chest and abdominal belts, bioimpedance principles, and inertial sensing units, are complemented by indirect methods derived from ECG and PPG signals. Hybrid systems, which are also discussed, represent a very promising approach. Special attention is paid to signal processing, machine learning, and multimodal sensor fusion algorithms that improve robustness and reliability. By systematically analyzing hardware and software combinations, validation protocols, and current limitations, this article identifies emerging trends in adaptive respiratory monitoring. This review aims to guide the development of next-generation wearable systems. Full article

Falls among older adults are a major public health concern, yet collecting large-scale real fall data for radar-based detection is ethically and practically difficult. This study presents a controlled simulation-to-real feasibility study for trip-fall detection using continuous-wave (CW) Doppler radar. The method couples a physics-informed kinematic trip-fall model with a CW radar observation model to synthesize I/Q signals and Doppler spectrograms, while domain randomization varies body size, fall direction, initial velocity, sensor placement, aspect angle, amplitude, and noise. Synthetic walking and respiration data were also generated for controlled three-class classification among trip fall, walking, and seated quiet breathing. In Experiment I, the simulated spectrograms reproduced the dominant time–frequency characteristics of measured enacted trip-fall signals acquired with a 24 GHz CW radar; quantitative similarity analysis yielded a mean SSIM of 0.782 and a Doppler-ridge MAE of 24.6 Hz across five fall directions. In Experiment II, a ResNet-18 classifier trained only on simulated spectrograms achieved a macro-F1 score of 0.912 [95% CI: 0.883–0.936] on measured data from ten participants, three start locations, and eight directions. Under the present controlled evaluation, this exceeded the available real-data-trained baseline of 0.748 [95% CI: 0.691–0.805] (paired subject-level permutation test, $p=0.006$). These findings suggest that physics-informed simulation with domain randomization can reduce dependence on real trip-fall samples under limited-data conditions. The results do not establish robustness to other fall morphologies, fall-like activities of daily living, different environments, different radar devices, or embedded deployment. Full article

Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes current applications of HRV metrics in wearable devices, including fitness tracking, mental stress assessment, sleep quality evaluation, and early detection of physiological or psychological disorders. Recent advances in photoplethysmography (PPG)-based HRV estimation have enabled noninvasive and user-friendly measurement, though challenges remain in accuracy under motion and variable environmental conditions. We also discuss methodological considerations, such as artifact correction, data segmentation, and the integration of HRV with other biosignals for multimodal analysis. Emerging research suggests that combining HRV with metrics such as respiration rate, skin conductance, and accelerometry can enhance robustness and interpretability in dynamic settings. Finally, future directions are proposed toward personalized health analytics, emotion-aware computing, and real-time adaptive feedback systems. This review highlights the growing potential of wearable HRV analysis as a foundation for preventive healthcare and human–machine symbiosis. Full article