Imaging Photoplethysmography (IPPG) is an emerging and efficient optical method for non-contact measurement of pulse waves using an image sensor. While the contactless way brings convenience, the inevitable distance between the sensor and the subject results in massive specular reflection interference on the skin surface, which leads to a low Signal to Interference plus Noise Ratio (SINR) of IPPG. To ease this challenge, this work proposes a novel modulation illumination approach to measure the accurate arterial pulse wave via surface reflection interference isolation from IPPG. Based on the proposed skin reflection model, a specific modulation illumination is designed to separate the surface reflections and obtain the subcutaneous diffuse reflections containing the pulse wave information. Compared with the results under ambient illumination and constant supplemental illumination, the SINR of the proposed method is improved by 4.56 and 3.74 dB, respectively.

Imaging photoplethysmography (iPPG) is an emerging optical technique that allows for the contactless acquisition of arterial Blood Volume Pulse (BVP) signals from video recordings of the human skin. While iPPG offers a non-contact and convenient means for physiological monitoring, the accuracy of the extracted BVP signals remains limited. This limitation hinders its potential for advanced cardiovascular assessments, such as evaluations of arterial stiffness and cardiac function. To address this issue, we propose a novel physiologically informed Gaussian filtering method, based on the prior knowledge that the BVP waveform can be modeled as a mixture of multiple Gaussian components. Specifically, a set of physiological Gaussian kernels is employed to convolve the noisy iPPG signal, generating a Gaussian representation that emphasizes waveform components with physiological relevance. This representation is further refined by a Transformer-based neural network to reconstruct accurate BVP signals. Experimental results demonstrate a notable improvement in BVP accuracy, with the mean absolute error reducing from 0.25 to 0.08. This enhancement in iPPG precision highlights the potential of our approach for advanced medical applications.

During self-selected overground walking, we examined participant-level surface electromyography (sEMG) descriptors associated with Gross Motor Function Classification System (GMFCS) levels I–III in 38 ambulatory children with spastic cerebral palsy. Eight-muscle sEMG was summarized using a design-locked pool of 16 peak-normalized envelope and exploratory synergy features. Higher GMFCS level was associated most consistently with longer activation duty cycle, stronger distal and proximal peak-normalized co-activation, and greater inter-muscle spatial entropy. Directions were retained across sensitivity analyses. For secondary within-cohort discrimination, the 16-feature pool yielded a quadratic weighted kappa (QWK) of $0.837\pm 0.046$ across 50 repeated participant-level nested cross-validation evaluations. Repeating correlation reduction and four-feature selection inside every outer-training fold yielded QWK $0.849\pm 0.029$. Both estimates reuse the 38 children and describe resampling performance, not independent-population generalizability. The core-four specification was post hoc. In an open supplementary workbook containing 195 unique participant IDs, directional concordance was strongest for activation duty cycle and distal co-activation, but direct transfer of the internally fitted 16-feature and core-four models was poor (QWK 0.210 and 0.077). Without healthy controls or measured walking speed and stride length, these cross-sectional results identify descriptors associated with ambulatory GMFCS level, not cerebral-palsy-specific biomarkers or evidence of clinical predictive utility.