The field of remote physiological monitoring is moving towards more accurate and reliable methods of measuring vital signs, such as heart rate and blood pressure, using photoplethysmography (PPG) and other non-invasive techniques. Researchers are developing innovative solutions to address the challenges of scalability, interoperability, and performance in real-time remote monitoring systems. The use of machine learning models and deep learning techniques is becoming increasingly popular in this field, enabling the detection of inaccuracies in measurements and providing real-time warnings to users. Furthermore, there is a growing focus on the regulatory aspects of wearable health technologies, with a need for a more pluralistic and evolving framework to ensure safety and equity. Noteworthy papers in this area include: Gaze into the Heart, which introduces a novel comprehensive large-scale multi-view video dataset for rPPG and health biomarkers estimation. Design, Implementation and Evaluation of a Real-Time Remote Photoplethysmography Acquisition System, which presents a real-time remote photoplethysmography system optimized for low-power devices. Improving Hypertension and Diabetes Outcomes with Digital Care Coordination and Remote Monitoring in Rural Health, which reports on a pre-post study of a comprehensive care coordination program including connected, wearable blood pressure and glucometer devices. Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices, which presents a real-time warning system that detects and communicates inaccuracies in PPG-derived HR. Bridging the Regulatory Divide: Ensuring Safety and Equity in Wearable Health Technologies, which proposes an approach emphasizing distributed risk, patient-centered outcomes, and iterative reform.