Part 3: Simultaneous Data Analysis and Interpretation: The web server sends the data to the back-end database server for storing and further analysis of the data. Sensor data is likely to be noisy. Therefore, the experts in engineering and pediatric obesity in minority populations gathered on this team will work together to create adaptive algorithms to distill important health and behavioral information from noisy sensors. All data will be analyzed and displayed in two modalities: 1) Average values over a week and 2) High and low points according to geographical position. To accomplish the first modality, accelerometery data will be reduced to display average time spent in sedentary, light, moderate and vigorous activity and average daily energy expenditure. Average heart rate, blood pressure, blood glucose and body temperature will be displayed along with the accelerometer data. We will use current literature and existing software examples to develop systems that will allow us to use Heart Rate and Galvanic Skin Response to measure and display stress and autonomic nervous system (ANS) function appropriately for these pediatric populations. We will fine-tune models for activity levels and correlate activity levels via accelerometry with our other sensing modalities (heart rate, blood pressure, blood glucose, etc.). An objective is to develop new signal processing tools based on multi-modal signal processing strategies to evaluate activity levels with the potential of developing a continuum of activity measures (sedentary, light, moderate and vigorous activity). The development of such algorithms will allow for accurate measurements even when one sensor is not working properly. In addition, we shall investigate the possibility of monitoring whether a child is properly wearing all sensors.