Abstract— The performances of the systems that fuse multiple data coming from different sources are deemed to benefit from the heterogeneity and the diversity of the information involved. In this work a novel Multi-Sensor Data Fusion (MSDF) architecture is presented. The estimation accuracy of the system states is reduced dramatically. Therefore, applying Kalman filter to generate the importance density function has been introduced to improve the performance with slightly increasing the computational complexity. In this paper we propose a new Multisensor based activity recognition approach which fuzzy logic fusion sensors to recognize Human Behavior. This approach aims to provide accuracy and robustness to the activity recognition system. In the proposed approach, we choose to perform fusion at the Feature-level.
Index Terms— Kalman filter, information fusion, multi-sensor data fusion, fuzzy logic, human activity detection.
Nattawut Wichit and Anant Choksuriwong are with the Department of Computer Engineering, Prince of Songkla University Songkla, Thailand (e-mail: 5410120071@email.psu.ac.th, anant.c@psu.ac.th).
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Cite: Nattawut Wichit and Anant Choksuriwong, " Multi-sensor Data Fusion Model Based Kalman Filter Using Fuzzy Logic for Human Activity Detection," International Journal of Information and Electronics Engineering vol. 5, no. 6, pp. 450-453, 2015.
