著者

山田 憲政 阿部 匡樹

出版者

日本認知科学会

雑誌

認知科学 (ISSN:13417924)

巻号頁・発行日

vol.7, no.4, pp.330-340, 2000-12-01 (Released:2008-10-03)

参考文献数

20

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Paintings convey static information in two-dimensional form. Human beings have tried to express three-dimensional space on a two-dimensional plane using techniques such as perspective. Attempts have also been made to express changes over time in humans and nature on a static plane. This procedure also requires some techniques for embodiment of the movements in a static canvas.In this study, we examined the embodied movement in the famous painting “The Milkmaid” by Vermeer. The painting was reconstructed in the three-dimensional space, and the movement embodied in the painting (i.e., the milk flowing from the jug) was analyzed.The results of analysis showed that the jug held by the maid must be moving slightly for the milk be to flowing from the jug. This implies that a slight arm movement was embodied by Vermeer in the maid's gesture in the painting, which is contrary to past interpretation of the painting.

著者

阿部 匡樹 山田 憲政

出版者

日本バイオメカニクス学会

雑誌

バイオメカニクス研究 (ISSN:13431706)

巻号頁・発行日

vol.2, no.2, pp.82-91, 1998

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Approximate Entropy (Ap entropy) was developed as a method to quantify regularity of time-series data. The aim of this study was to examine problems in the application of Ap entropy to human movement data and to distinguish movement patterns by quantifying the regularity of experimental human movement data. The following results were obtained: 1) For a relatively periodic time-series function with a small number of periods,difference in both the number of data points and number of periods affected the Ap entropy value. Thus,for the application of Ap entropy,the number of data points and the number of periods should be made the same. 2) Under the experimental conditions of this study,the change in the Ap entropy value was in accordance with subjective judgment of movement patterns. This indicates that Ap entropy is an effective parameter for quantitatively distinguishing movement patterns in data that differ in time-series regularity.