Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

• Published in: BioMed research international 2022, Vol.2022 (1), p.7020804-7020804
• Main Authors: Lv, Xinyu, Ta, Na, Chen, Tao, Zhao, Jing, Wei, Haicheng
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Ankle; Ankle Joint; Arrays; Arthritis; Biomechanical Phenomena; Care and treatment; Deep learning; Diagnosis; Entropy; Entropy (Physics); Gait; Gait Analysis; Heels; Humans; Joint diseases; Joint replacement; Joints (anatomy); Knee; Knee Joint - surgery; Latency; Methods; Neural networks; Noise; Patient outcomes; Patients; Physicians; Pose estimation; Posture; Rehabilitation; Risk factors; Statistical analysis; Target detection; Time series; Trajectory analysis; Walking
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2022/7020804

A gait feature analysis method based on AlphaPose human pose estimation fused with sample entropy is proposed to address complicated, high-cost, and time-consuming postoperative rehabilitation of patients with joint diseases. First, TensorRT was used to optimize the inference of AlphaPose, which consists of the target detection algorithm YOLOv3 and the pose estimation algorithm. It can speed up latency and throughput by about 2.5 times while maintaining the algorithm’s accuracy. Second, the optimized human posture estimation algorithm AlphaPose_trt was used to process gait videos of healthy people and patients with knee arthritis. The joint point motion trajectories of the two groups were extracted, and the sample entropy algorithm quantified the joint trajectory signals for feature analysis. The experimental results showed significant differences in the entropy of the heel and ankle joint motion signals between healthy people and arthritic patients (p