A Probabilistic Approach to Handle Missing Data for Multi-Sensory Activity Recognition

Context and activity recognition in complex scenarios is prone to data loss due to disconnections, sensor failure, transmission problems, etc. This generally implies significant changes in the recognition performance. In the case of classifier fusion architecture faulty sensors can be removed from the recognition chain to overcome this issue. Alternatively, we can try to compensate or impute data to replace the missing signals. In this paper we proposed a probabilistic method for imputation of missing data based on conditional Gaussian distribution. Our method exploits the correlation among classifier outputs to infer missing values in a probabilistic manner. We assess the method performance using two datasets (car manufacturing and a daily activities scenarios) with three different configurations of sensors. Results show the advantages of the probabilistic estimation at the classifier decision level. Author