Analyzing macroscopic human behaviors using spatio-temporal data from sensor networks

The thesis of this dissertation is that by analyzing the temporal properties of sensor measurements, patterns generated by macroscopic behaviors can be discovered and used to form virtual sensors that can convert low-level sensor events into actionable knowledge. Macroscopic behaviors are defined as human activities and routines that evolve over large spaces and extended periods of time and can thus be learned only in an unsupervised manner. Our work is based on two key observations: (1) most human behaviors are sequences of very primitive actions or events that can either be sensed directly by sensors or indirectly by pre-processing the sensor data; and (2) the same type of human activity will often trigger a similar pattern of sensor events in space and time. Based on these observations, this dissertation introduces three common types of macroscopic behaviors and proposes methods for their recognition. The first concerns the problem of identifying periodic human activities. The second focuses on discovering classes of frequent Spatio-Temporal Activities (STAs) from location traces, namely areas that people consistently spend time at approximately the same time intervals every day. The third problem deals with the detection of simple group behaviors specified in the form of sequences of interactions. The ability to define virtual sensors for these macroscopic behaviors is in the core of the BehaviorScope system — a human-centric sensing system aiming to offer real-world services as assisted living and power efficiency in large buildings. In the latter case we show how our system can potentially reduce the electricity consumption of an office building by up to 33.8%.