Title: Behaviour recognition system of underground drilling operators based on MA-STGCN

Authors: Meng Cai; XiChao Wang; Baojiang Li; Haiyan Wang; Xiangqing Dong; Chen Guochu

Addresses: Shanghai DianJi University, 300 Shuihua Road, Pudong District, Shanghai 201306, China ' Shanghai DianJi University, 300 Shuihua Road, Pudong District, Shanghai 201306, China ' Shanghai DianJi University, 300 Shuihua Road, Pudong District, Shanghai 201306, China ' Shanghai DianJi University, 300 Shuihua Road, Pudong District, Shanghai 201306, China ' Shanghai DianJi University, 300 Shuihua Road, Pudong District, Shanghai 201306, China ' Shanghai DianJi University, 300 Shuihua Road, Pudong District, Shanghai 201306, China

Abstract: In the intricate operations of mining rods, precise behavior recognition is paramount for operational safety. Addressing target detection and posture feature extraction challenges, this study proposes a method that integrates attention mechanisms with a Spatial-Temporal Graph Convolutional Network. An efficient channel attention mechanism is introduced during target detection, allocating weights to each channel to adapt to diverse features accurately. Multihead attention modules are incorporated in posture feature extraction, effectively capturing critical behavioral information. Behavior classification is achieved through the SoftMax function. Experimental results demonstrate the method's accuracy of 95.3% and a recall rate of 91.6% on the custom mining dataset. On the NTU-RGB+D public dataset, the method significantly improves accuracy and recognition speed. This research provides an innovative approach to behavior recognition in complex environments, ensuring precise identification of various behaviors in real-world scenarios, safeguarding worker safety, and holding crucial implications for applying behavior recognition technology in industrial fields.

Keywords: drilling operation; attention mechanism; spatio-temporal graph convolution; behaviour recognition; pose estimation.

DOI: 10.1504/IJCSE.2025.143465

International Journal of Computational Science and Engineering, 2025 Vol.28 No.1, pp.71 - 86

Received: 16 Aug 2023
Accepted: 02 Feb 2024
Published online: 21 Dec 2024 *

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