Wearable sensing systems are becoming widely used for a variety of applications, including sports, entertainment, and military. These systems have recently enabled a variety of medical monitoring and diagnostic applications in Wireless Health. The need for multiple sensors and constant monitoring lead these systems to be power hungry and expensive, with short operating lifetimes. In this paper, we introduce a novel methodology that takes advantage of the influence of human behavior on signal properties and reduces those three metrics from the data size point of view. This, in turn, directly influences the wireless communication and local processing power consumption. We exploit intrinsic space and temporal correlations between sensor data while considering both user and system behavior. Our goal is to select a small subset of sensors to accurately capture and/or predict all possible signals of a fully instrumented wearable sensing system. Our approach leverages novel modeling, partitioning, and behavioral optimization, which consists of signal characterization, segmentation and time shifting, mutual signal prediction, and subset sensor selection. We demonstrate the effectiveness of the technique on an insole instrumented with 99 pressure sensors placed in each shoe, which cover the bottom of the entire foot, resulting in energy reduction of 56% to 96% for error rates of 5% to 17.5%.