Missing motion data recovery using factorial hidden Markov models
Missing motion data recovery using factorial hidden Markov models
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DOI：
10.1109/robot.2008.4543449
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
Dongheui Lee;D. Kulić;Yoshihiko Nakamura
中科院分区：
文献类型：
--
作者：
Dongheui Lee;D. Kulić;Yoshihiko Nakamura
This paper proposes a method to recover missing data during observation by factorial hidden Markov models (FHMMs). The fundamental idea of the proposed method originates from the mimesis model, inspired by the mirror neuron system. By combining the motion recognition from partial observation algorithm and the proto-symbol based duplication of observed motion algorithm, whole body motion imitation from partial observation can be achieved. The algorithm for missing data recovery uses the same basic strategy as the whole body motion imitation from partial observation, but requires more accurate spatial representability. FHMMs allow for more efficient representation of a continuous data sequence by distributed state representation compared to hidden Markov models (HMMs). The proposed algorithm is tested with human motion data and the experimental results show improved representability compared to the conventional HMMs.
