Lidar-Based Action-Recognition Algorithm for Medical Quality Control

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.