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— In this paper, we propose a novel kernel computation algorithm between time-series human motion data for online action recognition. The proposed kernel is based on probabilistic models called switching linear dynamics (SLDs). SLD is one of the powerful tools for tracking, analyzing and classifying human complex time-series motion. The proposed kernel(More)
A framework and methodology to realize robot-to-human behavioral expression is proposed. Human-robot symbiosis requires to enhance nonverbal communication between humans and robots. The proposed methodology is based on movement analysis theories of dance psychology researchers, namely Laban, Lamb and Kestenberg. Two experiments on robot-to-human behavioral(More)
This paper presents a recognition method of human daily-life action. The method utilizes hierarchical structure of actions and describes it as tree. We modelize actions by continuous Hidden Markov Models which output time-series features based on feature expression described by human. In this method, recognition starts from the root, compete the likelihoods(More)
This paper presents three behavior labeling algorithms based on supervised learning using accumulated pyroelectric sensor data in the living space. We summarize features of each algorithm to use them in combination matched to usage of the livelihood support application. They are (a)labeling algorithms based on switching model around a behavioral(More)