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Multisensor fusion strategies have been widely applied in human activity recognition (HAR) in body sensor networks (BSNs). However, the sensory data collected by BSNs systems are often uncertain or even incomplete. Thus, designing a robust and intelligent sensor fusion strategy is necessary for high-quality activity recognition. In this article, Dezert-Smarandache theory (DSmT) is used to develop a novel sensor fusion strategy for HAR in BSNs, which can effectively improve the accuracy of recognition. Specifically, in the training stage, the kernel density estimation (KDE)-based models are first built and then precisely selected for each specific activity according to the proposed discriminative functions. After that, a structure of basic belief assignment (BBA) can be constructed, using the relationship between the test data of unknown class and the selected KDE models of all considered types of activities. In order to …