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Improved Long Short-Term Memory Network with Multi-Attention for Human Action Flow Evaluation in Workshop

1
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
2
Shanghai Space Propulsion Technology Research Institute, Shanghai 201620, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(21), 7856; https://doi.org/10.3390/app10217856
Received: 19 September 2020 / Revised: 30 October 2020 / Accepted: 2 November 2020 / Published: 5 November 2020
As an indispensable part of workshops, the normalization of workers’ manufacturing processes is an important factor that affects product quality. How to effectively supervise the manufacturing process of workers has always been a difficult problem in intelligent manufacturing. This paper proposes a method for action detection and process evaluation of workers based on a deep learning model. In this method, the human skeleton and workpiece features are separately obtained by the monitoring frame and then input into an action detection network in chronological order. The model uses two inputs to predict frame-by-frame classification results, which are then merged into a continuous action flow, and finally, input into the action flow evaluation network. The network effectively improves the ability to evaluate action flow through the attention mechanism of key actions in the process. The experimental results show that our method can effectively recognize operation actions in workshops, and can evaluate the manufacturing process with 99% accuracy using the experimental verification dataset. View Full-Text
Keywords: intelligent monitoring; human factors; action recognition; long short-term memory network; attention mechanism intelligent monitoring; human factors; action recognition; long short-term memory network; attention mechanism
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MDPI and ACS Style

Yang, Y.; Wang, J.; Liu, T.; Lv, X.; Bao, J. Improved Long Short-Term Memory Network with Multi-Attention for Human Action Flow Evaluation in Workshop. Appl. Sci. 2020, 10, 7856. https://doi.org/10.3390/app10217856

AMA Style

Yang Y, Wang J, Liu T, Lv X, Bao J. Improved Long Short-Term Memory Network with Multi-Attention for Human Action Flow Evaluation in Workshop. Applied Sciences. 2020; 10(21):7856. https://doi.org/10.3390/app10217856

Chicago/Turabian Style

Yang, Yun, Jiacheng Wang, Tianyuan Liu, Xiaolei Lv, and Jinsong Bao. 2020. "Improved Long Short-Term Memory Network with Multi-Attention for Human Action Flow Evaluation in Workshop" Applied Sciences 10, no. 21: 7856. https://doi.org/10.3390/app10217856

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