Deep learning models have become a powerful tool in knee angle estimation for lower limb prostheses, owing to their adaptability across various gait phases and locomotion modes. Current methods utilize Multi-Layer Perceptrons (MLP), Long-Short Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN), predominantly analyzing motion information from the thigh. Contrary to these approaches, our study introduces a holistic perspective by integrating whole-body movements as inputs. We propose a transformer-based probabilistic framework, termed the Angle Estimation Probabilistic Model (AEPM), that offers precise angle estimations across extensive scenarios beyond walking. AEPM achieves an overall RMSE of 6.70 degrees, with an RMSE of 3.45 degrees in walking scenarios. Compared to the state of the art, AEPM has improved the prediction accuracy for walking by 11.31%. Our method can achieve seamless adaptation between different locomotion modes. Also, this model can be utilized to analyze the synergy between the knee and other joints. We reveal that the whole body movement has valuable information for knee movement, which can provide insights into designing sensors for prostheses. The code is available at https://github.com/penway/Beyond-Gait-AEPM.

翻译：深度学习模型因其在不同步态阶段和运动模式中的适应性，已成为下肢假体膝关节角度估算的有力工具。当前方法利用多层感知机（MLP）、长短期记忆网络（LSTM）和卷积神经网络（CNN），主要分析来自大腿的运动信息。与这些方法不同，本研究引入了一种整体视角，将全身运动作为输入进行集成。我们提出了一种基于Transformer的概率框架，称为角度估算概率模型（AEPM），该模型能够在行走之外的广泛场景中提供精确的角度估算。AEPM的整体均方根误差（RMSE）为6.70度，其中行走场景下的RMSE为3.45度。与现有技术相比，AEPM在行走场景下的预测精度提升了11.31%。我们的方法能够在不同运动模式之间实现无缝切换。此外，该模型可用于分析膝关节与其他关节之间的协同作用。我们揭示出全身运动包含膝关节运动的有价值信息，这可为假体传感器设计提供启发。相关代码已开源在 https://github.com/penway/Beyond-Gait-AEPM。