Greeting Gesture Classification Using Machine Learning Based on Politeness Perspective in Japan
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- Wibowo Angga Wahyu
- Tokyo Metropolitan University
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- Kurnianingsih
- Politeknik Negeri Semarang
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- Saputra Azhar Aulia
- Tokyo Metropolitan University
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- Sato-Shimokawara Eri
- Tokyo Metropolitan University
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- Takama Yasufumi
- Tokyo Metropolitan University
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- Kubota Naoyuki
- Tokyo Metropolitan University
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抄録
<p>Understanding traditional culture is important. Various methods are used to achieve better cross-cultural understanding, and certain researchers have studied human behavior. However, behavior does not always represent a culture. Therefore, our study aims to understand Japanese greeting culture by classifying it through machine learning. Following are our study contributions. (1) The first study to analyze cultural differences in greeting gestures based on the politeness level of Japanese people by classifying them. (2) Classify Japanese greeting gestures eshaku, keirei, saikeirei, and waving hand. (3) Analyze the performance results of machine and deep learning. Our study noted that bowing and waving were the behaviors that could symbolize the culture in Japan. In conclusion, first, this is the first study to analyze the eshaku, keirei, saikeirei, and waving hand greeting gestures. Second, this study complements several human activity recognition studies that have been conducted but do not focus on behavior representing a culture. Third, according to our analysis, by using a small dataset, SVM and CNN methods provide better results than k-nearest neighbors (k-NN) with Euclidean distance, k-NN with DTW, logistic regression and LightGBM in classifying greeting gestures eshaku, keirei, saikeirei, and waving hand. In the future, we will investigate other behaviors from different perspectives using another method to understand cultural differences.</p>
収録刊行物
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 28 (2), 255-264, 2024-03-20
富士技術出版株式会社
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詳細情報 詳細情報について
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- CRID
- 1390581003356441600
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- NII書誌ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL書誌ID
- 033391182
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
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- 抄録ライセンスフラグ
- 使用不可