Initial Validation Of Withings Pulse Wave Velocity And Body Composition Scale

Mobile devices, wearable technology, smartphone apps, and home fitness tracking have become popular to monitor personal health. While cardiovascular diseases are the leading cause of mortality, these devices have become a part of people’s lives as they continuously collect health data to help prevent and manage chronic diseases. Research shows increased arterial stiffness is positively correlated to increased risk of cardiovascular disease. Gold standard for measuring arterial stiffness is pulse wave velocity which is usually measured by SphygmoCor. Withings Body-Cardio scale is a new method in measuring pulse wave velocity and can be used daily. This study is a validation test comparing Withings scale to the gold standard method using SphygmoCor. Subjects in this study had body composition calculated on the BodPod and had it compared to the measurements by Withings scale. Standing blood pressure was taken manually and with SphygmoCor. Heart rate and pulse wave velocity were measured by SphygmoCor and were compared to the measurements taken by the Withings scale. The results indicated that pulse wave velocity measurements taken by the two methods were not clinically different and Withings scale accurately calculates pulse wave velocity. This suggests individuals can use this scale daily to assess cardiovascular health

Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning

Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-world requires it to be robust to various environmental uncertainties. Existing works focus on finding Nash Equilibrium or the optimal policy under uncertainty in one environment variable (i.e. action, state or reward). This is because a multi-agent system itself is highly complex and unstationary. However, in real-world situation uncertainty can occur in multiple environment variables simultaneously. This work is the first to formulate the generalised problem of robustness to multi-modal environment uncertainty in MARL. To this end, we propose a general robust training approach for multi-modal uncertainty based on curriculum learning techniques. We handle two distinct environmental uncertainty simultaneously and present extensive results across both cooperative and competitive MARL environments, demonstrating that our approach achieves state-of-the-art levels of robustness