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This paper proposes a behavior prediction system for supporting our daily lives. The behaviors in daily-life are recorded in an environment with embedded sensors, and the prediction system learns the characteristic patterns that would be followed by the behaviors to be predicted. In this research, the authors applied a method of discovering time-series(More)
This paper describes applying image recognition techniques to the stained image captured by wound blotting. The wound blotting adsorbs the proteins on the wound surface and visualizes protein distribution as a stained image. The local patterns of the stained image may indicate wound healing. For investigation of relationship between pressure ulcer healing(More)
In this paper, a novel approach is proposed to recover human body pose from 3D voxel data. The use of voxel data leads to viewpoint-free estimation, which benefits in that reconstruction of a training model is needless in different multi-camera arrangements. Other notable aspects of our approach are real-time ensuring speed (up to 30[FPS]), flexibility(More)
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)
In this paper we propose a novel method for predicting resident's behaviors in a house from one's movement trajectories. The method consists of 1) segmentation of trajectory data into staying or moving and classification of the segments and 2) prediction by time-series association rules from transition events of each segment. The method predicts the start(More)