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6 February 2024 Study on health monitoring system of deep foundation pit operator of the transmission line based on residual network
Jun Xu, Lei Qian, Caiming Yin, Wenrui Wang, Peilun Zhang, Jinlong Qi, Min Xie
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Proceedings Volume 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023); 1297950 (2024) https://doi.org/10.1117/12.3015290
Event: 9th International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 2023, Guilin, China
Abstract
In response to the challenges of monitoring operators' health status in the complex environment of deep foundation pit construction sites for transmission lines, we propose a monitoring system based on a residual network. Firstly, obtain the electrocardiogram (ECG) signal of the operator based on the wearable ECG detection sensor. ECG data sent to the edge computing gateway by low-power Bluetooth networking. Secondly, the health status evaluation model of operators is constructed based on residual networks. Then calculate the data pre-processing and health state evaluation of the gateway on the edge. Finally, the evaluation results are sent to the remote server via 4G/5G network. Realize the operational warning of the operator and send the early warning information to the monitoring app. Experimental results demonstrate that introducing the volume block attention module (CBAM) into the residual block enhances the model's attention mechanism, resulting in improved recognition accuracy and recall rates. The proposed system effectively meets the real-time health status evaluation needs of operators, ensuring their safety amidst the challenging and complex environmental conditions encountered during deep foundation pit construction at transmission line sites.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jun Xu, Lei Qian, Caiming Yin, Wenrui Wang, Peilun Zhang, Jinlong Qi, and Min Xie "Study on health monitoring system of deep foundation pit operator of the transmission line based on residual network", Proc. SPIE 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 1297950 (6 February 2024); https://doi.org/10.1117/12.3015290
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KEYWORDS
Electrocardiography
Arrhythmia
Deep learning
Feature extraction
Machine learning
Data modeling
Education and training
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