Learning indoor movement habits for predictive control

Using Wi-Fi signals is an attractive and reasonably affordable option to deal with the currently unsolved problem of widespread tracking in an indoor environment. Our system, history aware-based indoor tracking system (HABITS) models human movement patterns and this knowledge is incorporated into a discrete Bayesian filter to predict the areas that will, or will not, be visited in the future. These probabilistic predictions may be used as an additional input into building automation systems for intelligent control of heating and lighting. This paper outlines current indoor tracking methods and the weaknesses associated with them. It describes in detail the operation of the HABITS algorithm and discusses the implementation of this algorithm in relation to indoor Wi-Fi tracking with a large wireless network. Testing of HABITS shows that it gives comparable levels of accuracy to those achieved by doubling the number of access points. It is twice as accurate as existing systems in dealing with signal black spots and it can predict the final destination of a person within the test environment almost 80% of the time.