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Multimodal information fusion decision-making strategy for personnel behavior in industrial scene
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Haiquan WANG1, Haowei YU2, Yueyi YANG1, Xiaobin XU3, Xiangzhou BU4, P KURKOVA5
China Safety Science Journal | 2025, 35(8) : 84 - 92
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China Safety Science Journal | 2025, 35(8): 84-92
Safety engineering technology
Multimodal information fusion decision-making strategy for personnel behavior in industrial scene
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Haiquan WANG1, Haowei YU2, Yueyi YANG1, Xiaobin XU3, Xiangzhou BU4, P KURKOVA5
Affiliations
  • 1College of Intelligent Sensing and Instrumentation, Zhongyuan University of Technology, Zhengzhou Henan 450007, China
  • 2School of Automation and Electrical Engineering, Zhongyuan University of Technology, Zhengzhou Henan 450007, China
  • 3College of Automation, Hangzhou Dianzi University, Hangzhou Zhejiang 310018, China
  • 4Henan Hongbo Measurment and Control Co., Ltd., Zhengzhou Henan 450040, China
  • 5School of Electronic and Laser Instrument, Saint Petersburg State University of Aerospace Instrumentation, Saint Petersburg 14-51, Russia
Published: 2025-08-28 doi: 10.16265/j.cnki.issn1003-3033.2025.08.0084
Outline
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In order to reduce the accidents in industrial scenarios which were caused by workers'unsafe operation behaviors, meanwhile improve the performance of visual-based action recognition methods in industrial scene with poor lighting, limited field of view and occlusions, an improved decision-making strategy based on self-adaptive ER (S-ER) was introduced in this paper. This strategy could integrate video information and inertial measurement unit (IMU) information effectively. It firstly analyzed video information and IMU information with attention mechanism-based multi-task convolutional 3D (M-C3D) model as well as one-dimensional convolutional neural network (1D-CNN) fused with attention mechanism, then ER theory was introduced to achieve decision-level fusion, where the set of evidence weights and reliability under different environmental conditions was optimized through the firefly optimization algorithm for improving the recognition accuracy and robustness of the model. The effectiveness of the proposed algorithm was verified on the public dataset Multimodal Human Action Dataset from University of Texas at Dallas(UTD-MHAD) and the self-built dataset Multimodal Human Action Dataset from Zhongyuan University of Technology(ZUT-MHAD). The results show that the identification results of S-ER for workers'unsafe behaviors in complex industrial scenarios can reach up to 98.53%, which is 17.52% higher than the maximum value of traditional multimodal fusion methods and single-modality recognition methods.

industrial scene  /  multimodal information  /  information fusion  /  action recognition  /  evidence reasoning (ER) theory
Haiquan WANG, Haowei YU, Yueyi YANG, Xiaobin XU, Xiangzhou BU, P KURKOVA. Multimodal information fusion decision-making strategy for personnel behavior in industrial scene[J]. China Safety Science Journal, 2025 , 35 (8) : 84 -92 . DOI: 10.16265/j.cnki.issn1003-3033.2025.08.0084
Year 2025 volume 35 Issue 8
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doi: 10.16265/j.cnki.issn1003-3033.2025.08.0084
  • Receive Date:2025-03-01
  • Online Date:2026-07-09
  • Published:2025-08-28
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  • Received:2025-03-01
  • Revised:2025-05-13
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Affiliations
    1College of Intelligent Sensing and Instrumentation, Zhongyuan University of Technology, Zhengzhou Henan 450007, China
    2School of Automation and Electrical Engineering, Zhongyuan University of Technology, Zhengzhou Henan 450007, China
    3College of Automation, Hangzhou Dianzi University, Hangzhou Zhejiang 310018, China
    4Henan Hongbo Measurment and Control Co., Ltd., Zhengzhou Henan 450040, China
    5School of Electronic and Laser Instrument, Saint Petersburg State University of Aerospace Instrumentation, Saint Petersburg 14-51, Russia
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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