首页|Lidar-Based Action-Recognition Algorithm for Medical Quality Control

Lidar-Based Action-Recognition Algorithm for Medical Quality Control

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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.

ambient intelligencelidarhuman action recognitiondeep learningmedical care

Wang Yuanze、Zhang Haiyang、Wu Xuan、Kong Chunxiu、Ju Yezhao、Zhao Changming

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School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China

Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education,Beijing 100081,China

Key Laboratory of Information Photon Technology,Ministry of Industry and Information Technology,Beijing 100081,China

2024

10.3788/LOP231732

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子：1.153
ISSN：1006-4125
年,卷(期)：2024.61(12)
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