用骨骼肌肉模拟生成运动数据，提升康复训练的智能评估效果
Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation
- 通过骨骼肌肉仿真生成逼真惯性传感器数据
- 在4个数据集上准确率提升，少样本微调更有效
- 适合康复、运动训练中的小样本智能评估场景
自动化运动质量评估可为物理治疗和体育训练提供客观、实时反馈。然而，基于惯性测量单元（IMUs）的深度学习模型常受限于数据稀缺、类别不平衡和标签模糊问题。本文提出一种基于骨骼肌肉仿真的数据增强方法，通过系统性修改运动轨迹生成IMU数据，强制满足解剖学合理的运动学约束，并结合逆运动学参数与知识驱动策略实现自动标注。在四个不同复杂度的数据集上，增强后的数据与真实数据高度相似，显著提升分类准确率、对未见受试者的泛化能力以及少量样本下的患者个性化微调效果。增益程度随数据集特性变化，尤其受类别平衡与标签模糊影响。结果表明，该方法能有效应对物理治疗中深度学习应用的常见挑战。
原文摘要 · Abstract (English)
Automated evaluation of movement quality can enhance physiotherapeutic treatment and sports training by providing objective, real-time feedback. However, deep learning models that assess movements captured by inertial measurement units (IMUs) are often limited by data scarcity, class imbalance, and label ambiguity. We present a data augmentation method that generates IMU data using musculoskeletal simulations integrated with systematic modifications of movement trajectories. The approach enforces anatomically plausible kinematic constraints and enables automatic labeling by combining inverse kinematic parameters with a knowledge-based evaluation strategy. Across four datasets of varying complexity, augmented variants closely resemble real-world data and contribute to gains in classification accuracy, generalization to unseen subjects, and patient-specific fine-tuning from few examples. The magnitude of these gains varies with dataset properties, in particular class balance and label ambiguity. These findings indicate that musculoskeletal simulation-based augmentation can address common challenges faced by deep learning applications in physiotherapeutic exercise evaluation.
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