用步态数据直接预测髋部肌肉力与关节力矩，提升临床应用效率。
Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics
- 基于三种序列模型，从步态角度数据预测髋部动力学参数。
- Transformer模型在健康人群上表现最优，力矩预测误差仅0.11 Nm/kg。
- 零样本外推测试中仍保持较好泛化能力，适合临床前研究。
通过多步速条件下的60名健康成年人步态数据，构建统一深度学习基准，直接从下肢关节角度预测髋部肌肉力与关节力矩。输入为10个双侧关节角度，输出为OpenSim生成的参考值。对比LSTM、Transformer和Mamba三种模型，在相同分割、预处理与评估协议下，Transformer在健康受试者中表现最佳：肌肉力预测RMSE=1.33 N/kg，MAE=0.57 N/kg，R²=0.819；关节力矩预测RMSE=0.11 Nm/kg，MAE=0.07 Nm/kg，R²=0.862。在未重新训练的情况下，对9名股骨头坏死患者进行零样本验证，仍保持中等预测能力（肌肉力RMSE=1.51 N/kg，R²=0.537；力矩RMSE=0.17 Nm/kg，R²=0.569）。结果表明，从步态数据推断髋部动力学是可行的，且Transformer可作为可靠基线，但需进一步病理验证与泛化能力提升。
原文摘要 · Abstract (English)
Estimating hip muscle forces and joint moments during gait typically relies on musculoskeletal simulation, which is informative but time-consuming and difficult to apply in clinical settings. This study developed a deep learning framework to predict these hip dynamics parameters directly from lower-limb gait kinematics and compared three representative sequence models under a unified protocol. Gait data were collected from 60 healthy adults under three metronome-guided cadence conditions. Ten bilateral lower-limb joint angles were used as inputs, and OpenSim-derived hip muscle forces and hip joint moments were used as reference outputs. Three deep learning models of LSTM, Transformer, and Mamba were trained and evaluated using the same subject-level split, preprocessing pipeline, and metrics. The best model was then directly tested on an external cohort of 9 patients with osteonecrosis of the femoral head (ONFH) without retraining. In the healthy-subject benchmark, Transformer achieved the best subject-level mean performance for both hip muscle force prediction (RMSE = 1.33 N/kg, MAE = 0.57 N/kg, R2 = 0.819) and hip joint moment prediction (RMSE = 0.11 Nm/kg, MAE = 0.07 Nm/kg, R2 = 0.862), with similar advantages across walking cadences. In zero-shot external validation, Transformer retained moderate predictive ability in ONFH for hip muscle force prediction (RMSE = 1.51 N/kg, MAE = 0.70 N/kg, R2 = 0.537) and hip joint moment prediction (RMSE = 0.17 Nm/kg, MAE = 0.12 Nm/kg, R2 = 0.569). These findings support the feasibility of estimating hip dynamics from gait kinematics, identify Transformer as a strong baseline, and highlight the need for broader pathological validation and improved generalization before clinical application.
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