國立虎尾科技大學 |

Human Behavior Understanding for Worker-Centered Intelligent Manufacturing.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Human Behavior Understanding for Worker-Centered Intelligent Manufacturing./
作者:	Tao, Wenjin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	164 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:	Dissertations Abstracts International82-04B.
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27993558
ISBN:	9798672110325

Human Behavior Understanding for Worker-Centered Intelligent Manufacturing.
Tao, Wenjin.

Human Behavior Understanding for Worker-Centered Intelligent Manufacturing. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 164 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2020.

This item must not be sold to any third party vendors.

In a worker-centered intelligent manufacturing system, sensing and understanding of the worker's behavior are the primary tasks, which are essential for automatic performance evaluation & optimization, intelligent training & assistance, and human-robot collaboration. In this study, a worker-centered training & assistant system is proposed for intelligent manufacturing, which is featured with self-awareness and active-guidance. To understand the hand behavior, a method is proposed for complex hand gesture recognition using Convolutional Neural Networks (CNN) with multiview augmentation and inference fusion, from depth images captured by Microsoft Kinect. To sense and understand the worker in a more comprehensive way, a multi-modal approach is proposed for worker activity recognition using Inertial Measurement Unit (IMU) signals obtained from a Myo armband and videos from a visual camera. To automatically learn the importance of different sensors, a novel attention-based approach is proposed to human activity recognition using multiple IMU sensors worn at different body locations. To deploy the developed algorithms to the factory floor, a real-time assembly operation recognition system is proposed with fog computing and transfer learning. The proposed worker-centered training & assistant system has been validated and demonstrated the feasibility and great potential for applying to the manufacturing industry for frontline workers. The developed approaches have been evaluated: 1) the multi-view approach outperforms the state-of-the-arts on two public benchmark datasets, 2) the multi-modal approach achieves an accuracy of 97% on a worker activity dataset including 6 activities and achieves the best performance on a public dataset, 3) the attention-based method outperforms the state-of-the-art methods on five publicly available datasets, and 4) the developed transfer learning model achieves a real-time recognition accuracy of 95% on a dataset including 10 worker operations.

ISBN: 9798672110325Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

Human behavior understanding

Human Behavior Understanding for Worker-Centered Intelligent Manufacturing.
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