Search Results (224)

Access to in-laboratory polysomnography (PSG) is restricted by prolonged waiting lists, and first-night effects can distort typical sleep architecture. This study evaluates the PneumoWave biosensor as a scalable, unobtrusive alternative for longitudinal single-channel home sleep apnea monitoring. Over three nights in an uncontrolled home environment, the biosensor’s respiratory rate agreement was evaluated against smartwatch-derived respiratory rate estimates, whilst apnea/hypopnea detection was compared with concurrent pulse oximetry. PneumoWave and smartwatch devices demonstrated good correlation (r = 0.870; p < 0.001). The PneumoWave device showed strong measurement agreement and provided a highly predictive screening pathway for patients with intermediate-to-high obstructive sleep apnea (OSA) risk. Longitudinal analysis confirmed consistent multi-night performance without first-night effect biases (ICC = 0.956, p < 0.001). Furthermore, its intuitive design yielded zero patient-induced setup errors, highlighting its operational robustness for self-administered use. Combining this continuous chest wall monitor with the STOP-Bang clinical questionnaire has the potential to provide an effective predictive screening pathway, improving community OSA screening and assisting clinical triage. Further validation against full polysomnography is warranted before clinical adoption. Full article

Automatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with computational efficiency remains challenging, particularly for resource-constrained systems. This paper proposes a lightweight two-stage Deep Learning framework for five-class sleep staging based on optimized multimodal physiological features extracted from electroencephalogram (EEG), electrooculogram (EOG), and photoplethysmography (PPG) signals. The framework is trained and tested using the Bitbrain Open Access Sleep (BOAS) database, considering a 31-subject dataset partitioned into training (24 subjects) and independent test (7 subjects) sets. Feature selection is performed using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by Principal Component Analysis (PCA) for EEG and EOG features, while respiratory and cardiac features derived from PPG are directly incorporated into the multimodal representation. A hierarchical Long Short-Term Memory (LSTM) architecture first classifies sleep into Wake, REM, and NREM, then further distinguishes the N1, N2, and N3 stages. On an independent test set, the classifier achieves 88.2% five-class accuracy on the multimodal feature set (EEG + EOG + PPG) with a model size of 3.14 MB, and 87.1% accuracy on the EEG-only feature set using only 2.95 MB of memory. Leave-One-Subject-Out (LOSO) cross-validation yields 86.9% accuracy, supporting subject-independent generalization. Inference latency ranged from 2.55 ms (EEG-only) to 4.02 ms (multimodal), with measured energy per inference of 2.88–10.9 mJ across feature sets. Additional validation on the RichSleep and ISRUC datasets demonstrates robustness across different recording conditions, achieving mean accuracies of 78.2% and 77.3%, respectively. The proposed framework provides a favorable trade-off among classification performance, complexity, and memory footprint, suggesting its potential suitability for wearable and edge-based sleep-monitoring systems. Full article

Smart textiles offer promising opportunities to address multiple aspects of relaxation-focused mental health interventions, which include monitoring and therapy. This paper presents a usability evaluation of a cushion-based smart textile system called Amiga, designed to guide users through breathing exercises that could be used in the future to support relaxation-focused interventions intended for independent use. A experimental user study with 20 participants was conducted to assess the system’s effectiveness, user performance, user experience, and overall satisfaction using structured interviews and validated usability questionnaires. The system achieved a mean System Usability Scale (SUS) score of 81.875 with standard deviation ($\sigma$ = 11.8), exceeding the established SUS benchmark of 68 and corresponding to the ‘Excellent’ category rating, under a predefined assistance-free criterion. The system was designed for respiratory phases (Ph) and respiration rate (RR) monitoring through a guided-breathing exercise where 90% completed with only 40% passing the quality criteria, achieving an average pace synchronization accuracy (PSA) of 61.2 % and phase duration accuracy (PDA) of 30% with a tolerance of ±1 s, thus providing a initial baseline for autonomous use and highlighting opportunities to improve on-boarding and in-application guidance. Most participants reported positive feedback regarding their confidence and overall satisfaction. Usability challenges were identified related to physical interaction with the cushion and clarity of on-screen instructions, providing clear directions for future design refinement. These findings demonstrate the feasibility of smart textile-based devices for home-based breathing exercises. Full article

The growing demand for wearable and Internet of Medical Things (IoMT) devices is driving the development of compact, low-power platforms for continuous physiological monitoring. Bioimpedance analysis represents a versatile non-invasive technique for the assessment of tissue properties, body composition, and respiratory dynamics. This work presents a comprehensive experimental validation of a compact bioimpedance measurement platform based on the SENSIPLUS chip, a CMOS sensor interface integrating a frequency-programmable lock-in amplifier for Electrochemical Impedance Spectroscopy in the 10 kHz–1 MHz range. The platform was validated at three complementary levels: (i) electrical characterization on Debye tissue-equivalent circuits using a three-point bilinear calibration, with analysis of the electrode–skin contribution and repeatability assessment; (ii) in vivo multi-frequency bioimpedance spectroscopy (BIS) with Cole–Cole model fitting and hook-effect correction; and (iii) single-frequency thoracic impedance plethysmography for respiratory monitoring. Results were compared against an Agilent E4980A precision Inductance (L), Capacitance (C), and Resistance (R) meter and a calibrated spirometer. The presented device achieved a maximum resistance error below 5.7% and reactance deviation under 6 $\mathsf{\Omega }$ across the investigated frequency range, Cole–Cole parameters consistent with reference values, and strong linear correlation ($R^2=0.97$) between thoracic impedance variations and tidal volume, with respiratory rate estimation errors below 2% across the ten sessions, specifically 1.43% during normal breathing and 1.96% during deep breathing. These results demonstrate that the SENSIPLUS-based platform achieves metrological performance compatible with the requirements of wearable IoMT applications, here demonstrated in a single-subject proof-of-concept study, while relying for all critical analog functions on a compact ($1.5\times 1.5$) mm² system-on-chip with low power consumption (1.5 mW). Full article

Pediatric sleep-disordered breathing (SDB) is underdiagnosed despite its associations with adverse neurobehavioral, psychosocial, and cardiometabolic outcomes. Pediatric dental providers routinely evaluate craniofacial growth and maintain longitudinal contact with children, yet practical approaches for integrating SDB risk assessment into dental settings remain limited. This protocol describes a prospective, cross-sectional observational study that will enroll 60 school-aged children aged 8–13 years from a university-based pediatric dental clinic and classify them as low-risk or high-risk for SDB using the Pediatric Sleep Questionnaire (PSQ; threshold ≥ 0.33). The primary aim is to examine associations between PSQ-based risk classification and objective physiologic sleep parameters, including the Apnea–Hypopnea Index, Respiratory Disturbance Index, Sleep Apnea Indicator, and Sleep Quality Index, obtained from a U.S. Food and Drug Administration-cleared wearable home sleep monitor—SleepImage Ring (MyCardio LLC, Denver, CO, USA)—worn for a minimum of three consecutive nights. Secondary aims will evaluate associations between SDB risk classification and body mass index, Mallampati score, Brodsky tonsillar grade, and psychosocial functioning (anxiety, depression, perceived stress, and daytime sleepiness). Exploratory craniofacial analyses will be conducted among participants with clinically available lateral cephalometric radiographs. This protocol could position pediatric dental visits as an accessible touchpoint for early identification of children with unrecognized SDB and inform pathways for timely referral. Protocol Version: 1.4, dated 13 May 2026. Trial Registration: ClinicalTrials.gov NCT07581938. Full article

Cognitive load is a key determinant of performance, safety, and learning in high-stakes environments. Self-report and performance-based methods remain valuable but often miss rapid within-task changes in mental demand, motivating non-invasive physiological sensing for continuous monitoring. The interpretability of these signals depends on their specificity, acquisition quality, signal processing requirements, and real-world feasibility. Integrating physiological mechanism, measurement performance, and operational feasibility, this article reviews non-invasive physiological metrics, including cardiovascular measures (HR/HRV), respiratory metrics, EEG, fNIRS, ocular metrics, and electrodermal activity, for cognitive load assessment across training, simulated, and operational contexts. This structured narrative review synthesized 37 sources published between January 2021 and February 2026, including 25 primary empirical studies and 12 systematic reviews or meta-analyses identified through Google Scholar, PubMed, Scopus, Web of Science, and IEEE Xplore. Among the primary studies, cardiovascular measures were most frequently used (HR/HRV, 16 of 25), followed by EEG (11), ocular metrics (9), electrodermal activity (8), fNIRS (3), and respiratory metrics (3). A consistent trade-off emerged between physiological specificity and ease of deployment. EEG frontal theta showed the most direct and meta-analytically supported link to cortical processing, but it is constrained outside controlled settings by motion artifacts and setup demands. Cardiovascular and electrodermal signals deploy easily through wearables but reflect broader autonomic or sympathetic activation rather than cognitive load specifically. No single metric reviewed here provides both high specificity and strong field readiness. A more defensible approach pairs signals deliberately, based on complementary mechanisms, signal-processing burden, and deployment context, rather than adding sensors indiscriminately. A tiered decision framework and an iterative, context-aware synthesis are proposed to guide metric selection and the interpretation of complementary measurements over time. Full article

Background/Objectives: Chronic dust-induced lung diseases, including pneumoconiosis and asbestosis, require long-term monitoring and individualized management. Conventional rehabilitation and follow-up often depend on in-person visits and may not provide continuous assessment outside clinical settings. This scoping review mapped evidence on digital technologies used to monitor patients, assess respiratory symptoms and functional status, and support rehabilitation follow-up in chronic dust-induced lung diseases. Methods: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Joanna Briggs Institute methodology. PubMed, Scopus, and Web of Science were searched from inception to 29 June 2026 without language restrictions. Five eligible publications were included. Results: The evidence was grouped into three categories: wearable monitoring of activity and respiratory symptoms, analysis of data from a digital rehabilitation platform, and electronic medical record (EMR) analysis. Wearable sensors showed high performance in recognizing basic activity states and cough events under controlled conditions. Platform- and EMR-based approaches showed potential for using clinical and functional data to support patient stratification and monitoring. However, most studies were limited to early technical or algorithmic validation and did not assess long-term home use, patient adherence, integration into clinical workflows, or clinical and rehabilitation outcomes. Conclusions: Digital technologies may support objective monitoring and rehabilitation follow-up in chronic dust-induced lung diseases, but the evidence base remains small and clinically immature. Prospective studies in real-world settings should evaluate usability, external validity, workflow integration, and clinically meaningful outcomes. Full article

Oncology care increasingly depends on heterogeneous sensing streams generated by computed tomography (CT), radiotherapy planning systems, wearable devices, home respiratory sensors, patient-reported outcomes, and clinical records. These data streams are often processed separately, limiting their value for longitudinal, context-aware review. This study proposes OncoSense-Agent, a reliability-aware agentic multimodal sensing architecture for CT-guided respiratory monitoring in oncology care. The architecture links CT-derived anatomical evidence with wearable physiology, respiratory symptoms, functional assessment, treatment context, and explainable human-in-the-loop review-priority generation. To move beyond a purely conceptual design, we implemented a lung-focused proof-of-concept with six bounded software agents: Imaging Reliability, Wearable Monitoring, Respiratory Review, Treatment Context, Multimodal Fusion, and Explainability. The prototype used real nnU-Net v2 3D lung segmentation metrics from 139 patients with complete bilateral lung CT data as the imaging anchor, while wearable, respiratory, symptom, and treatment-context channels were introduced as deterministic overlays for controlled validation. OncoSense-Agent changed review-priority assignment relative to CT-only assessment in 78/139 cases (56.1%), assigned 111/139 cases (79.9%) to high-priority or high-uncertainty tiers, and showed increasing Safety Gate activation as CT quality declined. Three illustrative cases demonstrate hidden respiratory deterioration, wearable data-quality uncertainty, and treatment-context risk not captured by CT-only assessment. The prototype does not establish clinical diagnostic accuracy, but demonstrates operational, auditable, reliability-aware multimodal review-priority generation for clinician-supervised oncology monitoring. Full article

A huge effort has been directed towards research and development of new measurement systems for maximizing comfort and safety in public buildings by monitoring indoor air quality (IAQ). In fact, according to World Health Organization, exposure to chemical, biological, and physical agents in poorly ventilated spaces can lead to psycho-physical discomfort as well as respiratory and neurological diseases. Recent advances in the Internet of Things (IoT) have paved the ground for the design and implementation of distributed measurement systems with higher sensor density and computational capacity. While these systems provide accurate assessments of individual rooms, they do not account for personal exposure to varying air quality levels over time. In public buildings such as schools, universities, and workplaces, occupants frequently move between rooms according to predefined schedules, resulting in heterogeneous exposure patterns. To address this issue, this paper proposes an innovative IAQ measurement technique for public buildings, shifting the focus from room-based assessment to occupant-centered assessment. Unlike wearable or portable personal monitors, the proposed technique infers occupant location from the institutional timetable and combines it with the fixed sensor infrastructure already installed in the rooms, requiring no additional devices to be worn. Individual conditions are quantified through a new personalized metric that integrates instantaneous air quality, cumulative individual exposure over time, and thermal comfort into a single index that is evaluated against occupant-specific thresholds. The technique is validated using real-world data, demonstrating higher potential to ensure safety and comfort compared to the state of the art. Full article

Background: Tobacco smoking remains one of the leading preventable causes of morbidity and mortality worldwide and is associated with cardiovascular, respiratory, oral, and psychological disorders. Although conventional smoking cessation interventions are effective, barriers related to accessibility, adherence, healthcare costs, and continuity of care frequently limit long-term success. Recent advances in technology may offer innovative opportunities to improve multidisciplinary smoking cessation pathways. Methodology: The Wear-Smile project is a non-profit, monocentric, randomized controlled trial registered in the Clinical Trial Protocol Registry and Results System (code No.: NCT07593742), designed to evaluate the effectiveness of a multidisciplinary telemedicine-based smoking cessation program assisted by wearable remote-monitoring devices. Adults who smoke combustible or heated tobacco or electronic nicotine delivery systems will be randomly allocated in a 1:1 ratio to either an experimental group receiving telemedicine-assisted rehabilitation integrated with wearable monitoring devices or a control group receiving standard in-person smoking cessation care. The intervention will include a multidisciplinary behavioral smoking cessation intervention involving cardiology, respiratory, dental, and psychology specialists. Primary outcomes will include Continuous Abstinence Rates and Point Prevalence Abstinence assessed at 6 and 12 months. Secondary outcomes will include cardiopulmonary and oral health parameters, smoking-related variables, health- and oral health-related quality of life (QoL), as well as adherence, usability, acceptability, and satisfaction with the rehabilitation program. Conclusions: The project may provide preliminary evidence regarding the potential feasibility, acceptability, and effectiveness of a multidisciplinary smoking cessation program integrating telemedicine and wearable monitoring technologies in improving abstinence outcomes, patient engagement, continuity of care, and QoL. Full article

Urban environments expose individuals to multiple stressors, including air pollution and noise, which significantly impact health by causing cardiovascular and respiratory diseases and sleep disruption. Effective monitoring of these stressors through intelligent sensing technologies can support the mitigation of long-term deterioration in both physical and mental health. In this context, this pilot study presents a multimodal approach that integrates environmental sensing and physiological monitoring to assess stress responses of the human body to urban conditions. Indoor and outdoor air quality were measured using smart sensor nodes that captured particulate matter (PM₁, PM_2.5, PM₄, PM₁₀), air temperature and relative humidity. The physiological response to urban noise exposure was evaluated using electrodermal activity (EDA) and heart rate variability (HRV) acquired via a wearable biomedical device, while sound pressure levels (dBA) were measured using a professional sound level meter. Preliminary results indicate that indoor particulate matter concentrations greatly exceeded outdoor levels, despite outdoor sensors being deployed in a high-traffic urban environment. Physiological analysis revealed increased tonic electrodermal activity under noise exposure, indicating increased sympathetic activation. Complementary HRV analysis showed elevated heart rate (HR), reduced parasympathetic activity, and increased sympathetic dominance under high-noise conditions, confirming a measurable physiological stress response to urban environmental exposure. Full article

The aim of this systematic review was to evaluate the validity and accuracy of wearable technologies used for monitoring physiological metrics in team-sport athletes. A systematic literature search was conducted in PubMed and Scopus databases, with additional studies identified through supplementary searching. Studies published between 2015 and 2025 were included if they assessed wearable devices in team-sport populations and compared their measurements with gold-standard methods. A total of eleven studies met the inclusion criteria. The findings indicate that heart rate monitoring demonstrates consistently high validity across different wearable devices, particularly in controlled laboratory conditions. In contrast, energy expenditure estimation shows substantial variability and systematic underestimation, especially during high-intensity and intermittent activities typical of team sports. VO₂max estimation presents mixed validity depending on device type and testing protocol, while respiratory frequency measurement demonstrates high agreement with gold-standard methods when assessed using specialized devices. Overall, wearable technologies provide valuable insights into athlete monitoring; however, their accuracy varies considerably depending on the physiological parameter and testing environment. These findings highlight the need for improved validation protocols and caution in the application of wearable-derived data in high-performance team-sport settings. Full article

Equine sports medicine is increasingly oriented toward objective, field-based monitoring systems that support both performance optimization and welfare assessment. In this context, smartphone-based technologies have emerged as accessible tools capable of integrating data acquisition, processing, and interpretation within a single platform. This narrative review aims to examine the role of smartphones in equine sports medicine, focusing on their function as standalone sensing devices and as gateways for wearable and external sensor systems. The analysis is based on a structured synthesis of current literature addressing technological foundations, including embedded sensors, connectivity architectures, and artificial intelligence-driven data processing, as well as their clinical applications across locomotor, cardiovascular, respiratory, behavioural, and thermoregulatory domains. Evidence indicates that smartphone-based systems improve the feasibility of longitudinal monitoring and facilitate real-time decision-making in field conditions, while enhancing communication between veterinarians, trainers, and owners. However, their performance remains influenced by acquisition conditions, system variability, and algorithmic constraints, requiring careful validation and contextual interpretation. In addition, challenges related to data governance, privacy, and ethical use remain insufficiently addressed. Overall, smartphone-based technologies represent enabling tools that support a transition toward more integrated, data-driven, and welfare-oriented management of the equine athlete, while highlighting the need for standardisation and regulatory development. Full article

Apnea of prematurity (AOP) remains a critical challenge in neonatal care, with caffeine citrate serving as the cornerstone of pharmacological intervention. However, the current standardized dosing schedule fails to account for significant inter-individual variability in caffeine pharmacokinetics and clinical response. This narrative review explores the transformative potential of integrating wearable sensor technologies and multi-modal data analytics into a closed-loop framework for personalized caffeine therapy. Based on a synthesis of current monitoring literature, we propose a theoretical, comprehensive monitoring system utilizing the area under the respiratory curve (rAUC) as a continuous proxy metric, alongside waveform amplitude analysis aligned with pediatric polysomnography standards. By incorporating emerging metrics such as respiratory rate variability (RRV) and hypoxic burden, the framework enables the objective quantification of respiratory stability. Furthermore, the integration of established neonatal intensive care unit (NICU) parameters for bradycardia and oxygen saturation detection provides a critical cross-validation layer to minimize artifact-induced false alarms. This conceptual model bridges the gap between advanced signal processing and clinical oversight, offering a scalable pathway toward precision dosing. By shifting from reactive to predictive neonatology, sensor-driven optimization can enhance therapeutic efficacy, reduce alarm fatigue, and ultimately improve developmental outcomes for preterm infants. Full article

Background: Near-infrared spectroscopy (NIRS)-based wearable devices offer non-invasive, continuous monitoring of muscle oxygenation, providing direct microvascular and metabolic information that complements indirect indices of intensity such as heart rate and accelerometry. Their clinical applicability in chronic non-communicable diseases (NCDs) remains under active development. Methods: A structured narrative review was conducted in PubMed, Scopus, Web of Science, and IEEE Xplore (January 2010–January 2026) using pre-specified search strings combining NIRS, muscle oxygenation, SmO₂, StO₂, wearable, exercise intensity, ventilatory/lactate threshold, and individual chronic disease terms. Eligible studies addressed technical validation of wearable NIRS, NIRS-derived exercise intensity estimation, clinical applications in NCDs, or rehabilitation implementation. Evidence was synthesized thematically; quality of validation studies was appraised against AMSTAR-2-informed, COSMIN-informed, or Cochrane RoB-2 criteria. Results: Wearable continuous-wave NIRS shows acceptable concurrent validity with frequency-domain laboratory systems (r = 0.79; range 0.69–0.88; ±8% SmO₂ agreement in 95% of measurements) and good test–retest reliability for moderate-to-severe domains (ICC 0.72–0.91). NIRS-derived breakpoints align more reliably with the second ventilatory/lactate threshold (ICC = 0.80) than with the first (ICC = 0.53), constraining its use for prescribing lower-intensity domains. In chronic obstructive pulmonary disease, peripheral arterial disease, chronic respiratory failure and selected cardiovascular conditions, wearable NIRS detects disease-specific patterns of muscle deoxygenation and post-exercise reoxygenation that track responses to rehabilitation. Conclusions: Current evidence supports wearable NIRS as a complementary, intensity-aware monitoring tool—particularly for delineating the heavy/severe-intensity boundary and detecting peripheral metabolic limitations—rather than as a stand-alone replacement for ventilatory or lactate thresholds. Because much of the evidence derives from small, single-sex or athlete-only cohorts, these findings should be regarded as a promising basis requiring further validation in broader NCD populations. Implementation in NCDs requires standardized placement and calibration protocols, sex- and body composition-stratified reference values, motion-artifact mitigation, and adequately powered longitudinal trials in clinical populations. Full article