  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
