In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models

Abstract

Activity recognition in a home setting is being widely explored as a means to support elderly people living alone. Probabilistic models using classical, maximum-likelihood estimation methods are known to work well in this domain, but they are prone to overfitting and require labeled activity data for every new site. This limitation has important practical implications, because labeling activities is expensive, time-consuming, and intrusive to the monitored person. In this article, the authors use Markov Chain Monte Carlo techniques to estimate the parameters of activity recognition models in a Bayesian framework. They evaluate their approach by comparing it to a state-of-the-art maximum-likelihood method on three publicly available real-world datasets. Their approach achieves significantly better recognition performance (p less than or equal to 0.05).