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- 山根 健
- 帝京大学理工学部
書誌事項
- タイトル別名
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- Estimation of Hand Motions Based on Distributed Representations and Neurodynamics
抄録
<p>Dynamical systems, which are described using differential equations, present numerous benefits for time-series information processing. They can accommodate continuous changes and dynamic features. However, they are not good for processing complex spatiotemporal patterns such as a temporal order of motions. Therefore, they are often combined with symbol-processing systems or discrete-event systems to produce hybrid systems. As described herein, we propose a method of processing sequences of elementary motions based only on distributed representations and a neurodynamical system. To assess the method’s possibilities, we constructed a human motion estimation system using a trajectory attractor model: a recurrent neural network with continuous-time dynamics. This system can deal analogically with novel hand and arm motions based on similarity between code patterns. Additionally, it can process complex sequences of motions in a robust manner because the network state is attracted to a long trajectory attractor formed in a series of subspaces corresponding to elementary motions. Then the network makes stable state transitions along the trajectory. Experimentally obtained results obtained from surface myoelectric signals show that the system estimated 15 complex hand and arm motions with average accuracy of about 86%, demonstrating the great potential of this system.</p>
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 32 (1), A-G43_1-12, 2017
一般社団法人 人工知能学会
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詳細情報 詳細情報について
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- CRID
- 1390282680083367424
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- NII論文ID
- 130005286785
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- ISSN
- 13468030
- 13460714
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可