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Human daily activity recognition is the foundation of automatic Ambient Assisted Living (AAL) system. In the paper, we propose a sensing model which can capture the discriminative spatio-temporal feature of human motion in an efficient way. The object space is separated into distinct discrete sampling cells by reference structure, and the ceiling mounted Pyroelectric infrared (PIR) sensors are used to capture the time-varying signal induced by human motion. The GMM-HMM model is utilized to classify different human activities. We use a self-developed PIR sensor node mounted on the ceiling to conduct experiments in a real office environment. Promising experimental results confirm the validity of our model.

Human Daily Activity Recognition Using Ceiling Mounted PIR Sensors