Abstract: Abstract: Hand hygiene is one of the most effective measures for preventing hospital infections， yet compliance among healthcare workers remains relatively low. While existing computer vision-based hand hygiene evaluation methods can perform step-by-step scoring of hand hygiene action， they still struggle with accurately perceiving and assessing fine movements. To address this issue， a novel hand hygiene assessment method that employs a segmentation module with multi-experts and an action contrast evaluation module is proposed. Through a two-stage evaluation process， the method aims to enhance the segmentation and assessment of fine movements， thereby enabling a more accurate evaluation of hand hygiene action quality. Specifically， the segmentation module with multi-experts learns the characteristics of each effective hand hygiene action firstly， and performs high-precision action segmentation reasoning based on characteristic information. Secondly， the action contrast evaluation module uses a contrastive learning approach that leverages the differences between example actions and current actions to calculate the action prediction score. Ultimately， the proposed method outputs predicted scores for each effective action and computes the final predicted score through comprehensive calculation. The proposed method achieves an action segmentation accuracy of 91.4% and a correlation coefficient of 0.864 for action quality assessment on the hand hygiene assessment dataset HHA300， both superior to existing hand hygiene assessment methods. Multiple comparative and ablation experiments demonstrate the effectiveness of each module in the method， indicating that it standardizes the hand hygiene assessment process and enables effective monitoring of hand hygiene action.

Key words: Key words: hand hygiene, action quality assessment, action segmentation, computer vision, multi-experts, contrastive learning