Application of naïve Bayes classifier in fall detection systems based on infrared depth sensors

A novel solution of the fall detection problem, based on the use of infrared depth sensors, is proposed. A methodology for acquisition of real-world data and their preprocessing is presented. The procedures for feature generation, preprocessing and selection are described. The naïve Bayes classifier is designed for the selected features and its performance is evaluated using a data set consisting of 144 sequences representative of 72 falls and 72 other human activities.