Mechanical Contact Conditions in Wearable Reflectance Photoplethysmography: Scoping Review.

Soft, Multi-Wavelength Photoplethysmography Enables Reliable Neonatal Blood Pressure Monitoring Via Error Stratification.

Continuous noninvasive blood pressure monitoring is important for neonatal hemodynamic management. However, cuffless photoplethysmography (PPG) is still difficult to use clinically because acquisition-related errors and their effects on blood pressure estimation are not well understood. Most studies report only overall error, which can hide error patterns under different signal-quality and motion conditions. Here, we developed a soft multi-wavelength wearable that combines reflective and transmissive optical paths to record complementary PPG signals. We also built NEO-BP, a prospective clinical dataset containing synchronized multi-wavelength PPG and invasive arterial blood pressure waveforms from 42 neonates. In a held-out segment-level test set without excluding samples based on signal quality (n = 9378), the green-red-infrared (G+R+IR) model achieved mean absolute errors of 9.67 mmHg for systolic blood pressure and 6.17 mmHg for diastolic blood pressure. Using this dataset, we established a retrospective descriptive subgroup error profile by stratifying estimation deviations across measurable acquisition and physiological conditions. Estimation errors increased under lower signal quality and stronger motion. These results identify error-prone acquisition states, support quality-aware interpretation of segment-level cuffless blood pressure estimates, and provide a basis for future models that estimate the error risk of individual readings.

Photoplethysmography acceleration indices as digital biomarkers of cardiometabolic health

Non-invasive monitoring of cardiovascular health is critical for early detection of dysfunction. Photoplethysmography (PPG) is already widely used in wearable devices to track cardiovascular signals, such as heart rate and pulse wave characteristics. The second derivative of the PPG signal, known as the accelerated plethysmogram (APG), provides indices reflecting arterial stiffness and vascular aging. Despite their potential as digital biomarkers, systematic comparisons across clinical populations are limited. In this cross-sectional study, four APG-derived indices (b/a, c/a, d/a, AGI) were analyzed in 133 participants: healthy individuals ( n  = 45), people with hypertension ( n  = 68), and people with diabetes ( n  = 20). All indices correlated significantly with chronological age within each group. Compared with healthy individuals, both pathological groups showed marked differences across indices, with large effect sizes. Direct comparison between participants classified as hypertensive and those classified as diabetic, with or without hypertension, revealed that the b/a ratio was the most discriminative parameter between these clinical groups (Cohen's | d | = 1.19). These findings indicate that APG indices differ significantly across health states and may represent non-invasive digital biomarkers of vascular health, supporting their integration into wearable devices for continuous cardiovascular monitoring.

[Computer-assisted sensing in periprosthetic joint infection : Current evidence and future perspectives for wearables].

Finger-based optical sensing for blood glucose monitoring using smartphone photoplethysmography

Accurate and continuous blood glucose monitoring remains a critical challenge due to the invasive nature of existing measurement techniques in remote and clinical settings. To address these challenges, this work presents a smartphonebased optical photoplethysmography (PPG) framework for low-cost, portable, and reliable blood glucose monitoring. Fingertip PPG signals are acquired from the left index finger using a smartphone camera for a duration of 15 s at 30 frames per second. The raw optical signal is extracted from the red colour channel, which is sensitive to blood volume variations dominated by haemoglobin absorption. Subsequently, a bandpass filter in the range of 0.5-5 Hz is used to isolate cardiacrelated pulsatile components. Baseline drift and motion-induced artifacts are attenuated using cubic spline interpolation while preserving PPG waveform morphology relevant to physiological analysis. From the pre-processed signals, time-domain and morphology-based PPG features are extracted and combined with demographic information and reference glucose values to construct the input dataset for glucose-level classification. The proposed framework is tested and evaluated using data collected from 80 subjects. Smartphone-acquired PPG signals are evaluated against the existing reference measurement system. The regression-based classification framework using XGBoost outperformed alternative algorithms, achieving a classification accuracy of 77%, precision of 85%, and numerical blood glucose prediction with a minimum RMSE of 15. The resultant findings demonstrated that smartphone based optical PPG represents a scalable noninvasive alternative for blood glucose monitoring, highlighting its potential for accessible remote monitoring applications.

A wearable device dataset for mental health assessment using laser Doppler flowmetry and fluorescence spectroscopy sensors.

BACKGROUND Mental health conditions such as depression, anxiety, and stress are commonly assessed using self-reported questionnaires and limited wearable physiological measures. However, reliance on subjective reporting, restricted sensor modalities such as heart rate variability and electrodermal activity, and small or homogeneous datasets may limit generalizability. We aimed to evaluate whether wearable optical sensing of microcirculation and tissue metabolism enables objective assessment of stress-related mental health states. METHODS We conducted a prospective observational study including 132 adults aged 18 to 94 years (58% female) from 19 countries. Participants underwent repeated fingertip measurements using a non-invasive wearable device combining laser Doppler flowmetry and fluorescence spectroscopy to capture microvascular perfusion and metabolic signals. Frequency-domain features were extracted using wavelet analysis. Depression, anxiety, and stress levels were assessed using a standardized 21-item questionnaire. Multiple machine learning models were evaluated under subject-wise validation, and model interpretability was assessed using Shapley-based feature attribution. RESULTS Here we show that ensemble-based models distinguish individuals with stress-related symptoms from those without with a receiver operating characteristic area under the curve of 0.72 and a precision-recall area under the curve of 0.89 under subject-wise validation. Microcirculatory variability and metabolic fluorescence features contribute substantially to prediction performance. Demographic variables, including sex, age, body mass index, and heart rate, are associated with increased stress-related risk. CONCLUSIONS Wearable optical sensing combined with interpretable machine learning provides physiological signatures associated with stress-related mental health conditions. This framework supports development of scalable and data-driven tools for objective mental health monitoring.

When is rPPG physiologically valid for stress and workload monitoring? A PRISMA-ScR scoping review

Remote photoplethysmography (rPPG) enables non-contact estimation of cardiovascular signals from ordinary cameras and has been increasingly applied to stress and mental workload assessment using heart rate (HR), heart rate variability (HRV), and pulse rate variability (PRV). However, reported performance varies substantially across studies, and stress-classification results are often presented without sufficient physiological validation, limiting interpretability and generalizability. This paper presents a PRISMA-ScR-guided scoping review of rPPG-based stress and mental workload research published between January 2010 and March 2026. Searches across Scopus, IEEE Xplore, PubMed, ACM Digital Library, and Google Scholar identified 20 empirical studies meeting predefined inclusion criteria (stress, mental workload, anxiety, and stress-related arousal outcomes); additional references were retained for background (methods, datasets/benchmarks, bias, and wearable comparisons) and were excluded from PRISMA counts. The literature is synthesized across laboratory, academic, online-interaction, and telehealth contexts, with emphasis on signal-processing pipelines, physiological targets, validation strategies, and deployment conditions. The reviewed evidence indicates that rPPG supports robust non-contact heart-rate estimation, while HRV/PRV reliability degrades under head motion, illumination variability, and short analysis windows commonly used for stress inference. Many studies rely on protocol labels or wearable references without agreement against electrocardiography or high-quality contact photoplethysmography, constraining physiological validity. Based on these findings, this review provides a validity-oriented synthesis and reporting recommendations to support reliable rPPG-based stress and workload assessment in ecologically relevant settings.