LAUSR

A reliable adaptive hybrid classifier ( hAHC ), which combines a posture-based adaptive signal segmentation algorithm with a multi-layer perceptron (MLP) classifier, together with a plurality voting approach, was proposed and evaluated in this study. The hAHC model was evaluated using a real-time posture recognition framework that sought to identify five behaviours (sitting, walking, standing, running, and lying) based on simulated crowd security scenarios. It was compared to a single MLP classifier ( sMLP ) and a static hybrid classifier ( hSHC ) from three perspectives (classification precision, recall and F1-score) that used the real-time dataset collected from unfamiliar subjects. Experimental results showed that the hAHC model improved the classification accuracy and robustness slightly more than the hSHC , and significantly more compared to the sMLP ( hAHC 82%; hSHC 79%; sMLP 71%). Additionally, the hAHC approach displayed the real-time results as animated figures in an adaptive window, in contrast to the hSHC which used a fixed size-sliding temporal window that as our results demonstrated, was less suitable for presenting real-time results. The main research contribution from this study has been the development of an efficient software-only-based sensor calibration algorithm that can improve accelerometer precision, together with the design of a posture-based adaptive signal segmentation algorithm that cooperated with an adaptive hybrid classifier to improve the performance of real-time posture recognition.