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Motion Analysis for Cooking Motion Recognition

Authors: Yuma Hijioka; Makoto Murakami; Tadahiko Kimoto;

Motion Analysis for Cooking Motion Recognition

Abstract

To construct a cooking motion model, we needed to analyze features of cooking motions. Our aim was to recognize cooking motions by tracking the motions of the joints of a cook's arms with a three-dimensional depth sensor. To recognize the motions, we needed to analyze their features of interest. We selected "cutting" and "mixing" as cooking motions of interest. Cutting is a motion where the cook's forearm moves up and down and the upper arm moves forward and back. Mixing is a motion where the cook's forearm moves as if drawing a wide circle in front of the body. Therefore, we focused on the motions of the forearm and upper arm as features of the cooking motion. With regard to the x-axis of the forearm, cutting had a small value for the logarithmic power, but mixing had a large value. This indicates a change in the same manner as the assumed motion. These results showed that the fifth logarithmic power of the x-axis can be used as a feature of cooking motions.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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