Reflective Photoplethysmography (PPG) sensors are less obtrusive than transmissive sensors, but they present patient-dependent variations in the so-called “Ratio of Modulation” (R). Thus, the conventionally employed calibration curves for determining peripheral oxygen saturation ( S_pO₂) may report inaccurate values. In this paper, we study the possibility of overcoming these limitations through Machine Learning (ML). For that, we show the results of applying several algorithms and feature combinations to PPG data from a human hypoxia study. The study was performed on ten healthy subjects. Their target oxygen saturation was reduced in five steps from 98- 100% to 70-77% through an oral mask. Blood Gas Analysis (BGA) was performed five times for each saturation level to measure the arterial oxygen saturation. PPG data were acquired from a reflective pulse oximeter placed in the subjects’ ear canals. PPG signals were pre-processed, and several features in the frequency and temporal domain were calculated. For the ML algorithms’ input, we explored different combinations of the features. We trained and validated the algorithms with the data from seven patients, and we tested them on three. Finally, we performed leaveone- out cross-validation to ensure the universality of the methods. The results show a good agreement of the predictions with the BGA values for Linear Regression, k- Nearest Neighbors, Stochastic Gradient Descent, and Neural Network for all input feature combinations with an average RMSE in the range of 3%. However, the performance of the Linear Regression was not beaten by the Neural Network, even for overfitting with 2000 hidden layers. The combination of R calculated with a Fast-Fourier Transform and ACRMS_.red/ACRMS_.irsignificantly improved the results, reducing the RMSE by 25%. This work demonstrates that a straight-forward Linear Regression is capable of determining S_pO₂with reflective PPG independently of the subject if the ratio ACRMS_.red/ACRMS_.ir is considered simultaneously with the Ratio of Modulation.

Photoplethysmography (PPG) is widely used in health monitoring, but its reliability is often compromised by artifacts, limiting accurate peripheral arterial oxygen saturation (SpO2) estimation. Moreover, physiological and demographic factors can substantially alter PPG waveform morphology. We propose a lightweight, real-time predictive modeling approach that adapts to subject-specific PPG signal dynamics to improve monitoring robustness under conditions prone to artifacts. A total of 459 min of dual-wavelength PPG signals, together with reference SpO2 values, were collected from 17 healthy volunteers (2 female, 15 male, mean age $27\pm 3$ years old) undergoing controlled desaturation in the 85–100% range after being instructed to remain still. Cardiac pulses were segmented and decomposed into AC and DC components, and the adequacy of several signal models, ranging from sums of Gaussians to Fourier series, and polynomial expansions of different orders, was evaluated. A space of representative signal features was built from the best-performing model, and used to generate machine learning-based predictions for each pulse using the preceding four clean pulses. Predicted pulses could be directly compared with their originals, enabling accurate error estimation without simulated data. The predicted signals closely matched the originals, achieving mean $R^2$ scores above 0.9, and an SpO2 estimation RMSE of 1.28%. In practical use, the same approach could be applied to overcome artifact-corrupted segments if combined with a signal quality assessment module. Therefore, this algorithm provides a promising pathway toward more reliable SpO2 monitoring in wearable systems, particularly under hypoxic conditions.

Neonatal intensive care units provide vital medical support for premature infants. The key aspect in neonatal care is the continuous monitoring of vital signs measured using adhesive skin sensors. Since sensors can cause irritation of the skin and lead to infections, research focuses on contact-free, camera-based methods such as infrared thermography and photoplethysmography imaging. The development of image processing algorithms requires large datasets, but recording the necessary data from studies brings tremendous effort and costs. Therefore, realistic patient phantoms would be feasible to create a comprehensive dataset and validate image-based algorithms. This work describes the realization of a neonatal phantom which can simulate physiological vital parameters such as pulse rate and thermoregulation. It mimics the outer appearance of premature infants using a 3D printed base structure coated with several layers of modified, skin-colored silicone. A distribution of red and infrared LEDs in the scaffold enables the simulation of a PPG signal by mimicking pulsative light intensity changes on the skin. Additionally, the body temperature of the phantom is individually adjustable in several regions using heating elements. In the validation process for PPG simulation, the feasibility of setting different pulse frequencies and the variation of oxygen saturation levels was obtained. Furthermore, heating tests showed region-dependent temperature variations between 0.19C and 0.81C around the setpoint. In conclusion, the proposed neonatal phantom can be used to simulate a variety of vital parameters of preterm infants and, therefore, enables the implementation of image processing algorithms for the analysis of the medical state.

Objective. Noninvasive measurement of oxygen saturation ( SpO ₂ ) using transmissive photoplethysmography (tPPG) is clinically accepted and widely employed. However, reflective photoplethysmography (rPPG)—currently present in smartwatches—has not become equally accepted, partially because the pathlengths of the red and infrared PPGs are patient-dependent. Thus, even the most popular ‘Ratio of Modulation’ ( R ) method requires patient-dependent calibration to reduce the errors in the measurement of SpO ₂ using rPPGs. Approach. In this paper, a correction factor or ‘pathlength ratio’ β is introduced in an existing calibration-free algorithm that compensates the patient-dependent pathlength variations, and improved accuracy is obtained in the measurement of SpO ₂ using rPPGs. The proposed pathlength ratio β is derived through the analytical model of a rPPG signal. Using the new expression and data obtained from a human hypoxia study wherein arterial oxygen saturation values acquired through Blood Gas Analysis were employed as a reference, β is determined. Main results. The results of the analysis show that a specific combination of the β and the measurements on the pulsating part of the natural logarithm of the red and infrared PPG signals yields a reduced root-mean-square error (RMSE). It is shown that the average RMSE in measuring SpO ₂ values reduces to 1 %. Significance. The human hypoxia study data used for this work, obtained in a previous study, covers SpO ₂ values in the range from 70 % to 100 %, and thus shows that the pathlength ratio β proposed here works well in the range of clinical interest. This work demonstrates that the calibration-free method applicable for transmission type PPGs can be extended to determine SpO ₂ using reflective PPGs with the incorporation of the correction factor β . Our algorithm significantly reduces the number of parameters needed for the estimation, while keeping the RMSE below the clinically accepted 2 %.

The body’s ability to balance oxygen supply and demand can be compromised in conditions such as shock, sepsis, and heart failure. Thus, measuring venous oxygen saturation (SvO2) simultaneously with the well-established peripheral arterial oxygen saturation can help in the clinical management of these conditions. Some authors have suggested a non-invasive SvO2 estimation method that acquires venous blood volume variations generated through the calf muscle pump using photoplethysmography (PPG): the Venous Muscle Pump Test (VMPT). However, the technique presents significant variability in the rhythm and speed of the foot dorsal flexions needed for the VMPT and cannot be performed on unconscious subjects and those with reduced mobility. This study proposes using functional electrical stimulation (FES) to stimulate the calf muscle and generate rhythmic and reproducible muscle contractions. A human proof-of-concept study was conducted with three healthy young male participants. The PPG signals achieved through the VMPT with conventionally active and FES-induced movements were compared. We found that FES-induced movement produced reproducible venous blood volume variations comparable to the ones induced by the active movement. However, it also leads to lower venous refilling time and lower muscle power. Although further individualized tuning of the stimulation parameters is needed to achieve more conclusive results, FES-induced movement proves to be a promising alternative to the conventional VMPT technique to measure venous oxygen saturation and assess venous insufficiency in specific clinical situations.

This paper presents a non-contact and cost-effective method to assess venous hemodynamics along the lower limbs using photoplethysmography imaging (PPGI). Seventeen healthy volunteers performed the venous muscle pump test, inducing venous blood volume changes in their lower legs, which were recorded using a webcam. PPGI signals were extracted from three regions along the lower leg. Key parameters derived from a physiological model were evaluated and analyzed statistically: perfusion amplitude, ejection time constant, and peripheral venous flow index. The method demonstrated robust estimation of physiologically explainable parameters, and the potential to improve venous function diagnostics with high spatial resolution.