  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.
