| Summary: | Sparse sensors that recognize full-body human motion and that control the motion of virtual humans have emerged as valuable research tools in the field of human–computer interactions. Here we propose a method for motion recognition and prolonged, continuous generation of motion data based on the recognition results. The only inputs required are the directional accelerations collected by four Wii remotes, which are attached on the four limbs of a human. The extended and continuous signal sequences are separated into small segments that can be described by particular motion content. Use of a fused hidden Markov model (FHMM) during the recognition process ensures the accuracy and efficiency with which independent motion segments are recognized. A graph model enhances the capacity of classification when dealing with a signal sequence associated with a prolonged motion. During the reconstruction and generation processes, an efficient state-based motion graph generates the extended and continuous virtual human motion data, which accurately reflects variation in the movement of the actors. Our method has a strong capacity to classify types of motion upon their recognition and the control process can be applied to a range of applications involving interaction.
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