Modeling Behavior As Mutual Dependency Between Physiological Signals And Indoor Location In Large-Scale Wearable Sensor Study

Wearable sensors today can unobtrusively collect rich time-series of physiological states and human movement patterns over a prolonged period. Gaining a better understanding of how an individual's physiological responses vary in different workplace environments can be valuable in understanding human behavior related to wellness and performance. In this work, we describe our exploration in discovering the correlation between one's physiological responses and movement patterns within different indoor locations using data collected from nurses in a hospital workplace for a ten week period. In this work, we use simple heuristics to empirically validate the idea that such a relationship may exist and then quantify it using mutual information analysis. We propose and demonstrate a data analysis approach that can also detect variations in the level of mutual dependency between different locations and physiological responses. The mutual dependency measures derived from our method are empirically shown to provide valuable information for improving modeling of self-reported work behavior patterns compared to using features derived from a single data stream.