A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle
  Force and Joint Kinematics
Yue Shi Shuhao Ma Yihui Zhao Zhiqiang Zhang
Muscle force and joint kinematics estimation from surface electromyography
(sEMG) are essential for real-time biomechanical analysis of the dynamic
interplay among neural muscle stimulation, muscle dynamics, and kinetics.
Recent advances in deep neural networks (DNNs) have shown the potential to
improve biomechanical analysis in a fully automated and reproducible manner.
However, the small sample nature and physical interpretability of biomechanical
analysis limit the applications of DNNs. This paper presents a novel
physics-informed low-shot learning method for sEMG-based estimation of muscle
force and joint kinematics. This method seamlessly integrates Lagrange's
equation of motion and inverse dynamic muscle model into the generative
adversarial network (GAN) framework for structured feature decoding and
extrapolated estimation from the small sample data. Specifically, Lagrange's
equation of motion is introduced into the generative model to restrain the
structured decoding of the high-level features following the laws of physics.
And a physics-informed policy gradient is designed to improve the adversarial
learning efficiency by rewarding the consistent physical representation of the
extrapolated estimations and the physical references. Experimental validations
are conducted on two scenarios (i.e. the walking trials and wrist motion
trials). Results indicate that the estimations of the muscle forces and joint
kinematics are unbiased compared to the physics-based inverse dynamics, which
outperforms the selected benchmark methods, including physics-informed
convolution neural network (PI-CNN), vallina generative adversarial network
(GAN), and multi-layer extreme learning machine (ML-ELM).