# Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG

arXiv: https://arxiv.org/abs/2207.01435 · HTML: https://mlsanity.com/paper/2207.01435/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2022.

## Built on by (most cited first, 18 of 18)

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- [Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations](https://mlsanity.com/paper/2405.01680/) (2024)
- [Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals](https://mlsanity.com/paper/2412.04213/) (2024)
- [A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics](https://mlsanity.com/paper/2307.05361/) (2023)
- [Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network](https://mlsanity.com/paper/2312.09418/) (2023)
- [sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics](https://mlsanity.com/paper/2408.16599/) (2024)
- [Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis](https://mlsanity.com/paper/2502.19056/) (2025)
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- [The bionic neural network for external simulation of human locomotor system](https://mlsanity.com/paper/2309.05863/) (2023)
- [Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics](https://mlsanity.com/paper/2412.05403/) (2024)
- [Deep Muscle EMG construction using A Physics-Integrated Deep Learning approach](https://mlsanity.com/paper/2503.05201/) (2025)
- [Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation](https://mlsanity.com/paper/2506.22459/) (2025)
- [Physics-Informed Learning for Human Whole-Body Kinematics Prediction via Sparse IMUs](https://mlsanity.com/paper/2509.25704/) (2025)
- [Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals](https://mlsanity.com/paper/2510.15371/) (2025)
- [Real-Time Musculoskeletal Surrogates for Pediatric Cerebral Palsy: a Credibility Pilot](https://mlsanity.com/paper/2608.28371/) (2026)

## Abstract (arXiv)

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moment) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods has emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. At the same time, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.