Simulation of human activity in a Health Smart Home with HMM

We propose to simulate data series of human activities, in order to provide benchmarking for human activity recognition algorithms. Within the French project “AILISA” we recorded 1492 days of data of activity collected with presence sensors in our experimental Health Smart Homes. We built a mathematical model on the data series, based on “Hidden Markov Models” (HMM) and Urn of Polya. The model was then played on a computer to produce simulated data series with flexibility to adjust the parameters in various scenarios. We tested several methods to measure the similarity between our real and simulated data and obtained better results when using the surface correlation.