This paper presents a novel framework of human activity recognition with time series collected from inertial sensors. We model each action sequence with a collection of Linear Dynamic Systems (LDSs), each LDS describing a small patch of the sequence. A codebook is formed by using the K-medoids clustering algorithm and a Bag-of-Systems (BoS) is developed to represent the time series. A great advantage of this method is that the complicated feature design procedure is avoided and the LDSs can well capture the dynamics of the time series. Our experiment validation on public dataset shows the promising results.
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