School of Electrical and Information Engineering, North Minzu University, Yinchuan 750021, China.
School of Information Engineering, Ningxia University, Yinchuan 750021, China.
Biomed Res Int. 2022 Apr 14;2022:7020804. doi: 10.1155/2022/7020804. eCollection 2022.
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 ( < 0.01), which can be used to identify patients with knee arthritis. This technique can assist doctors in determining needed postoperative joint surgery rehabilitation.
提出了一种基于 AlphaPose 人体姿态估计与样本熵融合的步态特征分析方法，以解决关节疾病患者术后康复复杂、成本高、耗时的问题。首先，使用 TensorRT 对包含目标检测算法 YOLOv3 和姿态估计算法的 AlphaPose 进行推理优化，在保持算法准确性的同时，将延迟和吞吐量提高约 2.5 倍。其次，使用优化后的人体姿态估计算法 AlphaPose_trt 处理健康人和膝关节炎患者的步态视频，提取两组的关节点运动轨迹，并使用样本熵算法对关节轨迹信号进行量化进行特征分析。实验结果表明，健康人和关节炎患者脚跟和踝关节运动信号的熵存在显著差异（<0.01），可用于识别膝关节炎患者。该技术可以帮助医生确定术后关节手术康复的需要。
