Comparison of fusion methods based on DST and DBN in human activity recognition

作     者：Andrei TOLSTIKOV Jit BISWAS Chris NUGENT Guido PARENTE 

作者机构：Institute for Infocomm ResearchNetworking Protocols Department1 Fusionopolis Way#21-01Singapore 138632 School of Computing and MathematicsUniversity of UlsterShore RoadNewtownabbeyBT37 0QBU.K. 

出 版 物：《控制理论与应用（英文版）》 (控制理论与应用)

年 卷 期：2011年第9卷第1期

页      面：18-27页

核心收录：

学科分类：08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 

主　　题：Dynamic Bayesian networks Dempster-Shafer theory Healthcare monitoring Ambient assisted living Activity recognition 

摘      要：Ambient assistive living environments require sophisticated information fusion and reasoning techniques to accurately identify activities of a person under care. In this paper, we explain, compare and discuss the application of two powerful fusion methods, namely dynamic Bayesian networks (DBN) and Dempster-Shafer theory (DST), for human activity recognition. Both methods are described, the implementation of activity recognition based on these methods is explained, and model acquisition and composition are suggested. We also provide functional comparison of both methods as well as performance comparison based on the publicly available activity dataset. Our findings show that in performance and applicability, both DST and DBN are very similar; however, significant differences exist in the ways the models are obtained. DST being top-down and knowledge-based, differs significantly in qualitative terms, when compared with DBN, which is data-driven. These qualitative differences between DST and DBN should therefore dictate the selection of the appropriate model to use, given a particular activity recognition application.