Medical-action recognition is crucial for ensuring the quality of medical services.With advancements in deep learning,RGB camera-based human-action recognition made huge advancements.However,RGB cameras encounter issu...Medical-action recognition is crucial for ensuring the quality of medical services.With advancements in deep learning,RGB camera-based human-action recognition made huge advancements.However,RGB cameras encounter issues,such as depth ambiguity and privacy violation.In this paper,we propose a novel lidar-based action-recognition algorithm for medical quality control.Further,point-cloud data were used for recognizing hand-washing actions of doctors and recording the action’s duration.An improved anchor-to-joint(A2J)network,with pyramid vision transformer and feature pyramid network modules,was developed for estimating the human poses.In addition,we designed a graph convolution network for action classification based on the skeleton data.Then,we evaluated the performance of the improved A2J network on the open-source ITOP and our medical pose estimation datasets.Further,we tested our medical action-recognition method in actual wards to demonstrate its effectiveness and running efficiency.The results show that the proposed algorithm can effectively recognize the actions of medical staff,providing satisfactory real-time performance and 96.3% action-classification accuracy.展开更多
文摘Medical-action recognition is crucial for ensuring the quality of medical services.With advancements in deep learning,RGB camera-based human-action recognition made huge advancements.However,RGB cameras encounter issues,such as depth ambiguity and privacy violation.In this paper,we propose a novel lidar-based action-recognition algorithm for medical quality control.Further,point-cloud data were used for recognizing hand-washing actions of doctors and recording the action’s duration.An improved anchor-to-joint(A2J)network,with pyramid vision transformer and feature pyramid network modules,was developed for estimating the human poses.In addition,we designed a graph convolution network for action classification based on the skeleton data.Then,we evaluated the performance of the improved A2J network on the open-source ITOP and our medical pose estimation datasets.Further,we tested our medical action-recognition method in actual wards to demonstrate its effectiveness and running efficiency.The results show that the proposed algorithm can effectively recognize the actions of medical staff,providing satisfactory real-time performance and 96.3% action-classification accuracy.
