A Multi-Scale and Multi-Stage Human Pose Recognition Method Based on Convolutional Neural Networks for Non-Wearable Ergonomic Evaluation
In the context of industrial robot maintenance and assembly, workers often suffer from work-related musculoskeletal disorders (WRMSDs). This paper proposes a multi-scale, multi-stage pose recognition method (MMARM-CNN) based on convolutional neural networks to provide ergonomic intervention. The met...
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Veröffentlicht in: | Processes 2024-11, Vol.12 (11), p.2419 |
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Sprache: | eng |
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Zusammenfassung: | In the context of industrial robot maintenance and assembly, workers often suffer from work-related musculoskeletal disorders (WRMSDs). This paper proposes a multi-scale, multi-stage pose recognition method (MMARM-CNN) based on convolutional neural networks to provide ergonomic intervention. The method leverages computer vision technology to enable non-contact data acquisition, reducing the interference of physiological and psychological factors on assessment results. Built upon the baseline yolov8-pose framework, the method addresses complex maintenance environments, which are prone to occlusion, by introducing the Lightweight Shared Convolutional Detection Head-pose (LSCD-pose) module, Multi-Scale Channel Attention (MSCA) mechanism, and Efficient Multi-Scale Patch Convolution (EMSPC) module, enhancing the model’s feature extraction capabilities. The MMARM-CNN model was validated using the MS COCO 2017 dataset and robot assembly data collected under laboratory conditions. The experimental results show that the MMARM-CNN achieved an accuracy improvement, reaching 0.875 in the mAP@0.5 evaluation. Overall, this method demonstrates significant potential in advancing the automation and intelligence of ergonomic interventions. |
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ISSN: | 2227-9717 2227-9717 |
DOI: | 10.3390/pr12112419 |