Search Results (89)

Continuous and noninvasive body fluid monitoring has attracted increasing attention in personalized healthcare, chronic disease management, and wearable point-of-care testing. Fiber-optic surface plasmon resonance (SPR) biosensors are particularly promising for this purpose because they combine label-free and real-time with miniaturization and low sample volume requirements. However, current body fluid sensing technologies and conventional bare metal SPR interfaces still face critical challenges, including insufficient analytical accuracy in complex biofluids, broad resonance linewidths, weak signal readability for trace biomarkers, and mechanical perturbations during wearable operation. These limitations highlight the need for nanomaterial-engineered fiber-optic SPR platforms that can convert interfacial molecular events into stable and sensitive signals. The review summarizes recent progress in nanomaterial-enabled fiber-optic SPR biosensors for continuous body fluid monitoring. Emphasis is first placed on nanomaterial mediated local electromagnetic field enhancement and plasmonic mode regulation. Subsequent discussion focuses on their functions in interfacial recognition, analyte enrichment, rapid mass transport, antifouling protection, and flexible integration for continuous operation. On this basis, representative sensing targets, material strategies, and device architectures for tears, urine, exhaled breath condensate, saliva and sweat are systematically analyzed. Finally, current challenges and future opportunities are discussed from the perspective of sensing reliability, wearable integration, and real sample validation. Full article

The metallic shell of converter transformer oil conservators prevents direct visual monitoring of the internal capsule, creating a long-standing monitoring blind spot that may leave hidden defects such as top gas accumulation undetected. To address this issue, this paper proposes an endoscopic video monitoring device installed inside the oil conservator capsule, enabling continuous visual monitoring without compromising insulation performance. On this basis, an anomaly detection method is established: region-of-interest (ROI) templates are defined in areas where the capsule normally adheres to the metallic inner wall of the oil conservator; normalized cross-correlation (NCC) is applied for template matching, and kernel density estimation (KDE) statistically analyzes the distribution of matching scores from normal samples to determine per-template thresholds. For valid template localizations, Euclidean distances among template centroids are extracted as geometric features and fed into an Isolation Forest for unsupervised anomaly scoring. Validation on field data from a 500 kV converter station demonstrates that the method achieves a precision of 98.78%, a recall of 96.43%, and an F1-score of 97.59%. The results indicate that the proposed method can distinguish normal capsule breathing from capsule morphological abnormalities caused by top gas accumulation, providing a basis for online monitoring of oil conservator capsules. Full article

Precise respiration assessment is crucial for heart rate variability (HRV) interpretation as respiratory components—particularly respiratory sinus arrhythmia (RSA)—provide essential information on vagally mediated regulation. Conventional single-lead electrocardiogram-derived respiration (EDR) methods measure the amplitude modulation of the QRS-waveform caused by respiratory chest movements. This causes a displacement of the electrical heart axis in relation to the ECG lead axis, typically within the 2D frontal plane of the Einthoven electrode montage. Another approach is based on heartbeat acceleration and deceleration during respective inspiration and expiration causing RR interval modulation. However, interval-based methods depend on the complexity of sympathovagal factors that affect RSA. The present feasibility study accounts for the 3D rotational movement of the electrical heart axis during the respiratory cycle and avoids non-respiratory neuromodulatory confounds. The beat-to-beat cardiac rotation was extracted from Frank-XYZ coordinates reconstructed via a four-electrode EASI device. In a pilot study with data from 19 healthy adults performing acoustically paced breathing (6–18 bpm), three surrogates (${\mathrm{RR-Interval}}_{\mathrm{EDR}}$, ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$) were compared using a unified Python 3.11.13 pipeline (3D VCG R-peak detection, multivariate Mahalanobis artifact correction, wavelet-based analysis) against a synthetic reference derived from the instructed breathing schedule. The results demonstrated a consistently lower estimation error and higher reference-based signal-to-noise ratio (refSNR), measuring spectral alignment with the paced-breathing trajectory for ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and achieving a mean refSNR of 6.01 dB (vs. 4.62 dB for ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ and 3.20 dB for ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$) and a mean absolute estimation error of 0.016 Hz (vs. 0.050 Hz and 0.032 Hz, respectively). Notably, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$ performance slightly improved at higher heart rates, consistent with the interpretation that higher cardiac sampling density benefits spectral resolution for chest movement-based methods, whereas ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ showed no significant heart rate dependence. Furthermore, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ was compared with the EDR results obtained by applying the Kubios-HRV Premium software (version 3.5.0). Kubios-EDR yielded higher precision at elevated breathing frequencies, whereas ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ outperformed Kubios-EDR at breathing rates below 10 bpm—a range that is particularly relevant for vagally activating slow breathing protocols or treatments. Future work should validate this method using a direct respiration measurement under spontaneous natural breathing conditions. Full article

Student mental health and academic stress are increasingly addressed through digital monitoring, yet evidence for personalized physiological thresholds based on exhaled VOCs, their in-the-wild feasibility, and their trigger–experience correspondence in everyday student life remains limited. This study examines whether exhaled breath signals can support personalized, real-world stress monitoring in university students using a two-stage design that moves from laboratory calibration to daily life validation. A total of 24 university students took part in the laboratory phase (Study 1; N = 24). Under two stress tasks, a social-conflict video task and a Stroop task, we derived an individualized breath-trigger threshold (θ_i) for each participant. We then invited 21 of them to join a three-day field deployment (Study 2; N = 21). Each participant’s θ_i from Study 1 was used directly as the trigger threshold for daily monitoring in order to test the association between trigger events and subjectively noticeable emotional deviations and to assess preliminary trigger–experience correspondence in daily life. The results show that 78.6% of paired trigger–EMA records were rated as subjectively salient, with 93.9% of these rated at medium-to-high intensity. These events occurred most frequently during study/work activities (60.6%), in dorm/home settings (57.6%), and when participants were alone (63.6%), suggesting that the triggers captured personally meaningful emotional episodes embedded in routine academic life rather than random physiological fluctuations. Overall, this study presents a portable breath-based emotion sampling device for student academic contexts and a reproducible protocol that combines laboratory thresholding with daily life validation. The findings provide preliminary and exploratory indications of the feasibility and within-person transferability of VOC-based emotion detection in students, and offer methodological support for future digital emotion monitoring and intervention design based on breath signals. 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

Disinfection is essential to ensure safe drinking water and hygienic conditions in environmental, industrial, and clinical settings. However, conventional methods for monitoring free residual chlorine are often laboratory-based and not suited for decentralized analysis. Here, we report a novel paper-based colorimetric biosensing platform that translates the ISO 7393-2 standard, a method based on the reaction of chlorine with N,N-diethyl-p-phenylenediamine (DPD), into a portable and user-friendly format. The proposed device integrates the DPD chemistry within a paper architecture, enabling reagent-free operation at the point of need. The sensor provides a rapid visual readout that is detectable by the naked eye, while quantitative analysis is achieved within 3 min through smartphone-based image acquisition. This work constitutes the first implementation of the ISO standard in a portable paper-based format suitable for both environmental and clinical matrices. The sensor provided a detection limit of 12 μM for sodium hypochlorite and was successfully validated in real samples, including bottled water and exhaled breath condensate, with satisfactory recoveries. Furthermore, the stability of the paper-based sensor was assessed under storage conditions of 4 °C and room temperature (23 °C), demonstrating excellent performance over 30 days in both cases, indicating that refrigeration is not required for maintaining sensor performance. Full article

Background: Pulse rate variability (PRV), a critical biomarker of autonomic nervous system (ANS) function, is typically evaluated using the pulse-to-pulse interval (PPI) signal extracted from a photoplethysmogram (PPG). Although PPGs have been widely used in wearable devices, the PPI signal is easily affected by motion artifacts or respiratory sinus arrhythmias (RSAs). These disturbances affect the accuracy of PRV for evaluating ANS function. The aim of this study was to remove the respiratory signals from raw PPI signals with an autoregressive moving average (ARMA) model. Methods: An R-wave to R-wave interval (RRI) sequence was extracted from the electrocardiogram (ECG). A self-made measurement system was used to record PPG, ECG, and respiratory signals. Nineteen healthy adults were recruited and requested to breathe with a spontaneous breathing rate (SBR) and control breathing rates (CBRs) (6, 18, and 30 breathing rate per minute, BRPM). Their ECG, PPG, and breathing signals were recorded for 6 min under different CBRs. The measurement was performed twice, i.e., eight measurements were performed. The raw RRI(t) and PPI(t) signals of 4 Hz were segmented into samples of one minute and shifted by 30 s. Thus, a subject had 80 samples, and there were 10 samples for each BRPM. RSA-free RRI signals were generated by a spectral method to filter RSA from raw RRI(t) to produce the target RRI(t). We proposed the individual subject ARMA models trained by samples with the maximum mean absolute errors between the target RRI(t) and raw PPI(t) (MAE_RAWs) of each subject, and the general model trained by samples with all maximum MAE_RAWs of all 19 subjects. Results: The mean absolute errors between the target RRI(t) and $\widetilde{PPI}(t)$ predicted by the individual subject ARMA models (MAE_{Subject-Models}) and general ARMA model (MAE_{General-Model}) were used to evaluate the performance of the two models. The results for the MAE_{Subject-Models} and MAE_{General-Model} were 132.5 ± 59.1 ms and 137.8 ± 67.8 ms, respectively, with no significant difference. MAE_{Subject-Models} and MAE_{General-Model} were compared with MAE_RAWs, whose attenuations (ATTs) were 28.5 ± 13.1% and 27.8 ± 12.6%, respectively. Conclusions: The two proposed models are capable of removing the RSA energy coupled in the raw PPI signals. Full article

Diagnostic latency limits time-sensitive care and early detection, and exhaled breath provides a rapid, repeatable window into metabolic and inflammatory chemistry. We review real-time breath sampling and analytical technologies and evaluate their readiness for clinical adoption, with emphasis on molecular pathways reflected in the breath volatilome and in exhaled breath condensate. Real-time mass spectrometry enables kinetic VOC profiling and targeted quantification, while humidity-aware sensors and wearable condensate platforms extend monitoring beyond the laboratory. Pathway-anchored interpretation links breath readouts to ketone handling, isoprenoid metabolism, nitric oxide signaling, lipid peroxidation, uremic nitrogen handling, and microbiome–host co-metabolism, but performance remains vulnerable to confounding, drift, and non-representative comparators. Translation requires standardized breath fraction control, traceable features, robust quality systems, and governed device algorithm stacks so that breath outputs inform decisions and outcomes. Full article

Despite many efforts to curtail drunk driving, alcohol-related traffic fatalities and injuries continue to be a major public health problem in the United States (U.S.) and most of the world. Technologies exist that prevent an automobile from starting if the driver’s breath alcohol exceeds 20 milligrams per deciliter (mg/dL), but these devices are only fitted to vehicles of individuals who have been convicted of Driving Under the Influence (DUI). A new approach must be taken to reduce the incidence of drunk driving by integrating an alcohol sensor system in vehicles as part of the delivered hardware. The system must be fast, accurate, and contactless—meaning that a forced exhalation is not required to measure the concentration of alcohol on the breath. We report on a novel device, the Driver Alcohol Detection System for Safety (DADSS) Breath Alcohol Sensor System, which uses the mid-infrared region of the electromagnetic spectrum to concurrently monitor alcohol and expired carbon dioxide (CO₂) to accurately quantify the breath alcohol concentration in samples that have been diluted in the atmosphere before being measured. The system was validated in a research laboratory with 70 male and female volunteers in 187 individual study days. Participants were given various doses of alcohol to consume and then breath and blood samples were collected simultaneously. Pearson correlation coefficients between the DADSS Breath Alcohol Sensor system and blood samples indicate a strong correlation between the measures, with an overall Pearson correlation of 0.8875 over an alcohol concentration range of 0–220 mg/dL. These results indicate that incorporating the DADSS system into motor vehicles has the potential to reduce the incidence of drunk driving. Full article

Background: Dental treatment often requires prolonged mouth opening. This may compromise comfort during spontaneous nasal breathing and saliva swallowing, leading to stress or anxiety. A high-flow nasal cannula (HFNC) delivers warmed and humidified air at high flow rates and may improve breathing comfort; however, the feasibility of its routine use during dental treatment has not been established. Objectives: The primary objective of this pilot study is to evaluate the feasibility of conducting a definitive clinical trial to investigate the use of a HFNC during dental treatment. The secondary objective is to explore preliminary patient-centered outcomes related to stress and comfort to inform the design of future clinical trials. Methods: This single-center, open-label pilot feasibility study will be conducted at Nagasaki University Hospital, with adult patients undergoing routine full-mouth periodontal treatment participating in two treatment sessions, one without a HFNC and one with a HFNC, separated by at least four weeks. The primary feasibility outcomes include recruitment and retention rates, patient tolerance and acceptability of the HFNC, completeness of data collection, and device-related adverse events. The secondary outcomes are exploratory and include physiological stress-related parameters (pulse rate, respiratory rate, autonomic nervous system indices, and electroencephalographic alpha wave activity) and patient-reported comfort assessed using a questionnaire. Conclusions: This pilot study was designed to assess the feasibility and safety of HFNC use during full-mouth periodontal treatment and to inform the design of future definitive clinical trials. In particular, the resultant exploratory patient-centered outcomes and preliminary data may be used to guide outcome selection and sample size estimation. Full article

Current gene-based PCR diagnostics involving reverse-transcription polymerase chain reaction (RT-PCR) require at least several hours, expensive tools, and complicated sample collection methods to obtain results. A test for detecting volatile organic compounds (VOCs) in exhaled breath is advantageous as a simple, non-invasive, and fast screening method. In this study, a VOC detection system of array sensors was applied for the classification of breath control and COVID-19 virus infection. The ability to classify VOCs in the breath with COVID-19 virus infection has been studied with two metal-oxide (MOX) gas sensor arrays, commercially available sensors, and in-house sensors. The dataset of gas response signals from the array-type semiconductive gas sensors of the VOC detection system was analyzed using machine learning; principal component analysis (PCA) was used as a dimensionality-reduction method, and random forest (RF) and a convolutional neural network (CNN) were used as classification methods for the VOC concentration patterns in each breath. For the RF model, the accuracy results for the classification by two gas sensor arrays was 0.917 and this was improved by CO₂ calibration to 0.967, and the feature importance analysis revealed the importance of specific gas sensors. For the CNN, an input layer of a transformed gray-scale image with the shape of 12 data points × 8 sensors was used, and its accuracy reached 100% within a relatively small number of epochs, demonstrating a short training time, which is beneficial for breath detectors or e-nose devices. Full article

Monitoring respiratory activity is a key indicator of physiological health and an essential component in smart textile systems for unobtrusive vital sign assessment. In this work, we present a one-dimensional convolutional recurrent neural network (1D-CRNN) for automatic classification of breathing activity from inertial data acquired by a smart e-textile of 59 subjects. The proposed method integrates convolutional layers for local feature extraction with recurrent layers for temporal context modeling, enabling robust segmentation of breathing and noise segments. The model was trained and evaluated using a stratified five-fold cross-validation scheme to account for inter-subject variability and class imbalance. Across different window sizes, the classifier achieved a mean accuracy of 0.88 and an F1-score of 0.92 at a window size of 2000 samples. The best-performing configuration for a single fold, reached an accuracy of 0.995 and an F1-score of 0.99. Furthermore, near-real-time feasibility was demonstrated, with a total processing time—including data loading, classification, segmentation, and visualization—of only 1.76 s for a 250 s measurement, corresponding to more than 100× faster than the recording time. These results indicate that the proposed approach is highly suitable for embedded, on-device inference within wearable systems. Full article

Exhaled breath analysis is emerging as one of the most promising non-invasive strategies for the early detection of life-threatening diseases, especially lung cancer, where rapid and reliable diagnosis remains a major clinical challenge. In this study, we designed and optimized an electronic nose (e-nose) platform composed of quantum resistive vapor sensors (vQRSs) engineered by polymer-carbon nanotube nanocomposites via spray layer-by-layer assembly. Each sensor was tailored through specific polymer functionalization to tune selectivity and enhance sensitivity toward volatile organic compounds (VOCs) of medical relevance. The sensor array, combined with linear discriminant analysis (LDA), demonstrated the ability to accurately discriminate between cancer-related biomarkers in synthetic blends, even when present at trace concentrations within complex volatile backgrounds. Beyond artificial mixtures, the system successfully distinguished real exhaled breath samples collected under challenging conditions, including before and after smoking and alcohol consumption. These results not only validate the robustness and reproducibility of the vQRS-based array but also highlight its potential as a versatile diagnostic tool. Overall, this work underscores the relevance of nanocomposite chemo-resistive arrays for breathomics and paves the way for their integration into future portable e-nose devices dedicated to telemedicine, continuous monitoring, and early-stage disease diagnosis. Full article

Electronic nose technology is attracting attention with its diagnostic applications in the healthcare field. In this study, respiratory samples of individuals with systolic heart failure (HFrEF) were analyzed using an electronic nose device to investigate the diagnostic feasibility for this disease. A total of 275 breath samples were collected from 29 patients and 31 healthy volunteers followed in a cardiology clinic. Classification using support vector machines (SVM) yielded an average accuracy rate of 85.21%. The simplicity of the statistical features used in the classification, combined with the low computational complexity, increases the method’s practicality. This study demonstrates that, unlike existing imaging and laboratory techniques, electronic nose technology can be considered a non-invasive, rapid, and cost-effective alternative for diagnosing heart failure, particularly notable for its potential to contribute to early diagnosis. Full article

Poor sleep quality and excessive daytime sleepiness are commonly reported by individuals with cystic fibrosis. The potential impact of comorbid sleep-disordered breathing (SDB), particularly obstructive sleep apnea (OSA), has not been extensively studied in the CF population. At present, there are no specific recommendations available to help clinicians identify patients with CF who are at increased risk of sleep disorders. Home sleep apnea testing using a validated peripheral arterial tonometry (PAT) device may offer an accurate diagnosis of OSA in a more convenient and low-cost method than in-lab polysomnography. In this single-center study of 19 adults with CF, we found an increased prevalence of OSA among individuals with CF compared to general population estimates. Although associations with an FEV < 70% predicted and a modified Mallampati score ≥ 3 were observed, these odds ratios did not reach statistical significance, likely reflecting limited power in this small pilot sample. There was no association found between the self-reported presence of nocturnal cough or snoring and OSA. We also found no association between OSA and abnormal scores on commonly used, validated sleep questionnaires, suggesting that CF-specific scales may be needed for effective screening in the CF clinic. Full article