用混合模型精准预测行走时的踝角与地面反作用力。
Hybrid LSTM-UKF Framework: Ankle Angle and Ground Reaction Force Estimation
- 结合LSTM与无迹卡尔曼滤波，融合多模态生物力学数据。
- 在3km/h时GRF预测RMSE降低18.6%，1km/h时踝角RMSE降低22.4%。
- 适合假肢、外骨骼等智能助行系统开发人员参考。
准确预测关节运动学与动力学对推进步态分析及智能助行系统（如假肢、外骨骼）的发展至关重要。本研究提出一种混合LSTM-UKF框架，用于在不同步行速度下估计踝角与地面反作用力（GRF）。通过融合测力台数据、膝关节角度与GRF信号，构建多模态传感器融合策略以丰富生物力学上下文。采用受试者特异性验证评估模型性能，使用均方根误差（RMSE）与决定系数（R²）作为指标。LSTM-UKF在所有条件下均优于独立的LSTM与UKF模型，在3 km/h时GRF预测的RMSE降低高达18.6%；同时，引入UKF显著提升了鲁棒性，相较于单独使用UKF，在1 km/h时踝角的RMSE降低达22.4%。结果表明，该混合架构在跨受试者与不同步速条件下具有优异的步态预测可靠性。
原文摘要 · Abstract (English)
Accurate prediction of joint kinematics and kinetics is essential for advancing gait analysis and developing intelligent assistive systems such as prosthetics and exoskeletons. This study presents a hybrid LSTM-UKF framework for estimating ankle angle and ground reaction force (GRF) across varying walking speeds. A multimodal sensor fusion strategy integrates force plate data, knee angle, and GRF signals to enrich biomechanical context. Model performance was evaluated using RMSE and $R^2$ under subject-specific validation. The LSTM-UKF consistently outperformed standalone LSTM and UKF models, achieving up to 18.6\% lower RMSE for GRF prediction at 3 km/h. Additionally, UKF integration improved robustness, reducing ankle angle RMSE by up to 22.4\% compared to UKF alone at 1 km/h. These results underscore the effectiveness of hybrid architectures for reliable gait prediction across subjects and walking conditions.
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