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The article discusses the development of an intelligent wearable device called the e-physiotherapist, which aims to measure physical activity (PA), blood pressure, and energy consumption. The device utilizes multiple sensors to collect data on various aspects of PA and cardiovascular health indicators. The goal is to develop and validate AI-based algorithms that can accurately measure these parameters in both laboratory and everyday conditions. The article highlights the limitations of current wearables on the market and emphasizes the need for a transparent and evidence-based wearable for healthcare use. The document discusses the use of wearable technology, specifically open-source wearables, to assess physical activity (PA) and health-related variables in real-time. The project aims to develop and validate algorithms using artificial intelligence and machine learning to analyze data collected from accelerometers and optical sensors in the wearables. The goal is to create a self-learning system that can accurately measure PA intensity, energy expenditure, and even blood pressure at an individual level. The project will involve recruiting apparently healthy adults to test the wearables and algorithms in controlled laboratory conditions and real-world settings. The document discusses the use of wearable technology and machine learning techniques to assess physical activity and energy expenditure. The goal is to develop a wearable device that can accurately measure and predict physical activity levels and provide personalized advice for healthy levels of activity. The document references several studies that have used machine learning to classify different types and intensities of physical activity with high accuracy. The authors also mention the use of reinforcement learning to handle unlabelled data [Extracted from the article]