Better-than-the-best fusion algorithm with application in human activity recognition

Nayeff Najjar; Shalabh Gupta

doi:10.1117/12.2177123

22 May 2015 Better-than-the-best fusion algorithm with application in human activity recognition

Nayeff Najjar; Shalabh Gupta;

Author Affiliations +

Proceedings Volume 9498, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015; 949805 (2015) https://doi.org/10.1117/12.2177123
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, Maryland, United States

Abstract

This paper introduces the Better-than-the-Best Fusion (BB-Fus) algorithm. The BB-Fus algorithm is a simple and effective information fusion algorithm that combines the information from different sources (be it sensors, features or classifiers) to improve the Correct Classification Rate (CCR). It can be observed that in most classification problems, different sensors or features might have different classification accuracies in separating different classes. Therefore, this paper constructs an optimal decision tree that isolates one class at a time with the best sensor to separate that particular class. The paper shows that the decision tree improves the overall CCR as compared to the use of any single sensor or feature for any 3-class classification problem. The efficiency of the BB-Fus algorithm is validated on the Opportunity data set to solve the human activity recognition problem where a set of 56 sensors (including a localization system, accelerometers, inertial measurement units and magnetic sensors mounted on various body parts; besides, accelerometers and gyroscopes mounted on different objects) are used. The CCR resulting from the BB-Fus algorithm is 96% while the best sensor achieved 94% CCR.

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Nayeff Najjar, Shalabh Gupta, "Better-than-the-best fusion algorithm with application in human activity recognition", Proc. SPIE 9498, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015, 949805 (22 May 2015); doi: 10.1117/12.2177123; https://doi.org/10.1117/12.2177123

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