On determining the best physiological predictors of activity intensity using phone-based sensors

Physical inactivity is a leading risk factor in worldwide deaths. This problem has led to the need for new research paradigms investigating the effect of sedentary behavior on negative health outcomes. Central to this need is the development of objective and ubiquitous sensors that provide accurate measurements of activity to assist in intervention. Phone-based kinematic sensors, such as accelerometers and gyroscopes, are one such option. Current kinematic sensor models have limited capability in adjusting for inter-personal physiological differences in the maps from movement to activity intensity since they focus on weight and height information. It would be useful to explore what features are the best descriptors for a population. We present a family of regression techniques that incorporate an arbitrary number of physiological features and use this framework to determine the best physiological features to map movement to energy expenditure. We do this for rest, treadmill and overground walking since these are the most common activities for which intervention is necessary. Size-based features, such as height, weight and BMI were the best descriptors for personalization. BMI was the best descriptor for rest and height was the best descriptor for walking. Fitness based features, such as resting energy expenditure and resting heart rate, were the least useful descriptors, particularly for walking.