基于多类GRF的步态障碍分类的3D数字孪生可视化

中文摘要 AI 辅助

自动化步态分析需要准确的分类和可解释的输出。我们提出了一个集成框架，用于使用双侧地面反作用力（GRF）和压力中心（COP）信号对健康步态和多种肌肉骨骼损伤组进行分类。信号在站立相进行归一化，并使用训练集统计数据进行标准化。该模型在会话级划分下实现了99.00%的验证准确率和90.07%的测试准确率。类别特定的ε-LRP识别了双侧、多个信号分量和不同站立相的正负贡献。另外，处理后的GRF信号和模型预测在基于Blender的3D可视化中同步，实现了对步态试验和分类结果的样本级检查。所提出的框架集成了分类、可解释性和3D可视化，以提高模型透明度。源代码可在以下仓库中获取：此https URL

英文摘要

Automated gait analysis requires accurate classification and interpretable outputs. We propose an integrated framework for classifying healthy gait and multiple musculoskeletal impairment groups using bilateral ground reaction force (GRF) and center-of-pressure (COP) signals. The signals were normalized over the stance phase and standardized using training-set statistics. The model achieved a validation accuracy of 99.00\% and a test accuracy of 90.07\% under a session-level split. Class-specific $ε$-LRP identified positive and negative contributions across both sides, multiple signal components, and different stance phases. Separately, the processed GRF signals and model predictions were synchronized within a Blender-based 3D visualization, enabling sample-level inspection of gait trials and classification results. The proposed framework integrates classification, explainability, and 3D visualization to improve model transparency. The source code is available in the following repository: https://github.com/nyoico/grf-gait-3d-visualization.git

发表机构

• Yonsei University（延世大学）

机构由 AI 辅助整理，请以论文原文为准。