Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight loss. We describe several challenges that arise in applying machine-learning techniques to the healthcare domain and present various solutions utilized in the armband system. We demonstrate how machine-learning and multisensor datafusion techniques are critical to the system's success. It is well recognized that regular and accurate self-monitoring of physiological parameters and energy expenditure (calorie burn) can improve self-awareness of personal health by providing important feedback. Such awareness and tracking are prerequisites for cost-effective health management, illness reduction, health-conscious decision making, and long-term lifestyle changes.