# Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation

arXiv: https://arxiv.org/abs/2506.22459 · HTML: https://mlsanity.com/paper/2506.22459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Correcting model misspecification in physics-informed neural networks (PINNs)](https://mlsanity.com/paper/2310.10776/) (2023)
- [Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG](https://mlsanity.com/paper/2207.01435/) (2022)
- [Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer](https://mlsanity.com/paper/2211.12315/) (2022)
- [Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals](https://mlsanity.com/paper/2412.04213/) (2024)

## Abstract (arXiv)

Accurately decoding human motion intentions from surface electromyography (sEMG) is essential for myoelectric control and has wide applications in rehabilitation robotics and assistive technologies. However, existing sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate, or purely data-driven models that lack physiological consistency. This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning, thereby preserving physiological consistency while achieving accurate motion estimation. The PENN employs a recursive temporal structure to propagate historical estimates and a lightweight convolutional neural network for residual correction, leading to robust and temporally coherent estimations. A two-phase training strategy is designed for PENN. Experimental evaluations on six healthy subjects show that PENN outperforms state-of-the-art baseline methods in both root mean square error (RMSE) and R2 metrics.