This paper aims to detect the potential injury risk of the anterior cruciate ligament (ACL) by proposing an ACL potential injury risk assessment algorithm based on key points of the human body detected using computer vision technology. To obtain the key points data of the human body in each frame, OpenPose, an open source computer vision algorithm, was employed. The obtained data underwent preprocessing and were then fed into an ACL potential injury feature extraction model based on the Landing Error Evaluation System (LESS). This model extracted several important parameters, including the knee flexion angle, the trunk flexion on the sagittal plane, trunk flexion angle on the frontal plane, the ankle knee horizontal distance, and the ankle shoulder horizontal distance. Each of these features was assigned a threshold interval, and a segmented evaluation function was utilized to score them accordingly. To calculate the final score of the participant, the score values were input into a weighted scoring model designed based on the Analytic Hierarchy Process (AHP). The AHP based model takes into account the relative importance of each feature in the overall assessment. The results demonstrate that the proposed algorithm effectively detects the potential risk of ACL injury. The proposed algorithm demonstrates its effectiveness in detecting ACL injury risk, offering valuable insights for injury prevention and intervention strategies in sports and related fields. Code is available at: https://github.com/ZiyuGong-proj/Assessment-of-ACL-Injury-Risk-Based-on-Openpose

翻译：本文旨在通过提出一种基于计算机视觉技术检测人体关键点的前交叉韧带（ACL）潜在损伤风险评估算法，以检测ACL的潜在损伤风险。为获取每一帧中的人体关键点数据，采用了开源计算机视觉算法OpenPose。对获取的数据进行预处理后，将其输入基于落地误差评估系统（LESS）构建的ACL潜在损伤特征提取模型。该模型提取了包括膝关节屈曲角、矢状面躯干屈曲角、额状面躯干屈曲角、踝膝水平距离以及踝肩水平距离在内的若干重要参数。每个特征均被赋予阈值区间，并采用分段评估函数进行相应评分。为计算参与者的最终得分，将评分值输入基于层次分析法（AHP）设计的加权评分模型。该AHP模型综合考虑了各特征在整体评估中的相对重要性。结果表明，所提算法能够有效检测ACL损伤风险，为运动及相关领域中的损伤预防与干预策略提供了有价值的见解。代码获取地址：https://github.com/ZiyuGong-proj/Assessment-of-ACL-Injury-Risk-Based-on-Openpose