# A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics

arXiv: https://arxiv.org/abs/2307.05361 · HTML: https://mlsanity.com/paper/2307.05361/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Highway Networks](https://mlsanity.com/paper/1505.00387/) (2015)
- [On distinguishability criteria for estimating generative models](https://mlsanity.com/paper/1412.6515/) (2014)
- [Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG](https://mlsanity.com/paper/2207.01435/) (2022)
- [FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2011.06104/) (2020)
- [A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2111.08685/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer](https://mlsanity.com/paper/2211.12315/) (2022)
- [Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network](https://mlsanity.com/paper/2110.07878/) (2021)

## Built on by (most cited first, 1 of 1)

- [The bionic neural network for external simulation of human locomotor system](https://mlsanity.com/paper/2309.05863/) (2023)

## Abstract (arXiv)

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).