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30 April 2022 Analysis of human's skilled process of assembly task using time-sequence-based machine learning
Ryo Miyoshi, Kosuke Kimura, Shuichi Akizuki, Manabu Hashimoto
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Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 1217719 (2022) https://doi.org/10.1117/12.2626117
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China
Abstract
To efficiently teach novices skilled tasks, it is necessary to analyze the difference between a novice and an expert worker. Accordingly, a method for extracting differences (on the basis of skill level) in motions of workers performing tasks is proposed. As for this method, a network (multi-stream LSTM) that estimates skill level from 3D positional information of the worker’s visual point and joints is trained, and the internal structure of the network is then analyzed. The results of an experiment indicate that a particular motion, namely, “grasping an object,” becomes different when the worker becomes skilled; in particular, the worker grasps the object without moving their visual point to the position of the part, namely, without looking at the object, and uses both hands efficiently.
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Ryo Miyoshi, Kosuke Kimura, Shuichi Akizuki, and Manabu Hashimoto "Analysis of human's skilled process of assembly task using time-sequence-based machine learning", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 1217719 (30 April 2022); https://doi.org/10.1117/12.2626117
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KEYWORDS
Visualization
Visual analytics
Machine learning
Motion analysis
Information visualization
Motion models
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