This paper presents BScope, a new system for interpreting human activity patterns using a sensor network. BScope provides a run-time, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. The users of the system are able to describe human activities with high level scripts that are directly mapped to hierarchical probabilistic grammars used to parse low level sensor measurements into high level distinguishable activities. Our approach is presented, though not limited, in the context of an assisted living application in which a small, privacy preserving camera sensor network of five nodes is used to monitor activity in the entire house over a period of 25 days. Privacy is preserved
by the fact that camera sensors only provide discrete high-level features, such as motion information in the form of image locations, and not actual images. In this deployment, our primary sensing modality is a distributed array of image sensors with wide-angle lens that observe people’s locations in the house during the course of the day. We demonstrate that our system can successfully generate summaries of everyday activities and trigger notifications at run-time by using more than 1.3 million location measurements acquired through our real home deployment.