Deep Muscle EMG construction using A Physics-Integrated Deep Learning approach

• URL: http://arxiv.org/abs/2503.05201v1
• Date: Fri, 07 Mar 2025 07:46:26 GMT
• Title: Deep Muscle EMG construction using A Physics-Integrated Deep Learning approach
• Authors: Rajnish Kumar, Tapas Tripura, Souvik Chakraborty, Sitikantha Roy,
• Abstract summary: We propose a hybrid deep learning algorithm, namely the neural musculoskeletal model (NMM), that integrates physics-informed and data-driven deep learning.<n>While data-driven modeling is used to predict the missing EMG signals, physics-based modeling engraves the subject-specific information into the predictions.
• Score: 0.7499722271664147
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Electromyography (EMG)--based computational musculoskeletal modeling is a non-invasive method for studying musculotendon function, human movement, and neuromuscular control, providing estimates of internal variables like muscle forces and joint torques. However, EMG signals from deeper muscles are often challenging to measure by placing the surface EMG electrodes and unfeasible to measure directly using invasive methods. The restriction to the access of EMG data from deeper muscles poses a considerable obstacle to the broad adoption of EMG-driven modeling techniques. A strategic alternative is to use an estimation algorithm to approximate the missing EMG signals from deeper muscle. A similar strategy is used in physics-informed deep learning, where the features of physical systems are learned without labeled data. In this work, we propose a hybrid deep learning algorithm, namely the neural musculoskeletal model (NMM), that integrates physics-informed and data-driven deep learning to approximate the EMG signals from the deeper muscles. While data-driven modeling is used to predict the missing EMG signals, physics-based modeling engraves the subject-specific information into the predictions. Experimental verifications on five test subjects are carried out to investigate the performance of the proposed hybrid framework. The proposed NMM is validated against the joint torque computed from 'OpenSim' software. The predicted deep EMG signals are also compared against the state-of-the-art muscle synergy extrapolation (MSE) approach, where the proposed NMM completely outperforms the existing MSE framework by a significant margin.

Related papers

• Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials [40.34973869620865]
  Digital sensing faces challenges in developing sustainable methods to extend the applicability of customized e-noses to body odor volatilome (BOV)<n>We developed MORE-ML, a computational framework that integrates quantum-mechanical (QM) property data of e-nose molecular building blocks with machine learning (ML) methods to predict sensing-relevant properties.
  arXiv  Detail & Related papers  (2026-01-01T22:56:07Z)
• Reconstruction of Surface EMG Signal using IMU data for Upper Limb Actions [0.7359962178534359]
  This paper investigates the synthesis of normalized sEMG signals from 6-axis IMU data using a deep learning approach.<n>A Sliding-Window-Wave-Net model, based on dilated causal convolutions, was trained to map the IMU data to the sEMG signal.<n>The results show that the model successfully predicts the timing and general shape of muscle activations.
  arXiv  Detail & Related papers  (2025-11-21T12:26:33Z)
• Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation [3.606446851103922]
  sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate.<n>This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning.
  arXiv  Detail & Related papers  (2025-06-17T16:07:20Z)
• BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals [50.76802709706976]
  This paper proposes Brain Omni, the first brain foundation model that generalises across heterogeneous EEG and MEG recordings.<n>To unify diverse data sources, we introduce BrainTokenizer, the first tokenizer that quantises neural brain activity into discrete representations.<n>A total of 1,997 hours of EEG and 656 hours of MEG data are curated and standardised from publicly available sources for pretraining.
  arXiv  Detail & Related papers  (2025-05-18T14:07:14Z)
• Equivariant Masked Position Prediction for Efficient Molecular Representation [6.761418610103767]
  Graph neural networks (GNNs) have shown considerable promise in computational chemistry.<n>We introduce a novel self-supervised approach termed Equivariant Masked Position Prediction (EMPP)<n>EMPP formulates a nuanced position prediction task that is more well-defined and enhances the learning of quantum mechanical features.
  arXiv  Detail & Related papers  (2025-02-12T08:39:26Z)
• Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals [4.382876444149811]
  This paper presents a physics-informed deep learning method to predict muscle forces without any label information during model training.<n>In addition, the proposed method could also identify personalized muscle-tendon parameters.<n>The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods.
  arXiv  Detail & Related papers  (2024-12-05T14:47:38Z)
• MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network [21.596126531908908]
  Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart.<n>We introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model.
  arXiv  Detail & Related papers  (2024-11-28T04:25:28Z)
• AI-Aided Kalman Filters [65.35350122917914]
  The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
  arXiv  Detail & Related papers  (2024-10-16T06:47:53Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks [40.33075223431487]
  Metallic Glasses (MGs) are widely used materials that are stronger than steel while being shapeable as plastic. We utilize Graph Neural Networks (GNNs) to model MGs and study EBs. We contribute a new dataset for EB prediction and a novel Symmetrized GNN (SymGNN) model that is E(3)-invariant in expectation.
  arXiv  Detail & Related papers  (2023-12-08T04:27:11Z)
• A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics [4.878073267556235]
  Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. This paper presents a novel physics-informed low-shot learning method for sEMG-based estimation of muscle force and joint kinematics.
  arXiv  Detail & Related papers  (2023-07-08T23:01:12Z)
• A Neural PDE Solver with Temporal Stencil Modeling [44.97241931708181]
  Recent Machine Learning (ML) models have shown new promises in capturing important dynamics in high-resolution signals. This study shows that significant information is often lost in the low-resolution down-sampled features. We propose a new approach, which combines the strengths of advanced time-series sequence modeling and state-of-the-art neural PDE solvers.
  arXiv  Detail & Related papers  (2023-02-16T06:13:01Z)
• Differentiable modeling to unify machine learning and physical models and advance Geosciences [38.92849886903847]
  We outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG) "Differentiable" refers to accurately and efficiently calculating gradients with respect to model variables. Preliminary evidence suggests DG offers better interpretability and causality than Machine Learning.
  arXiv  Detail & Related papers  (2023-01-10T15:24:14Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Heterogeneous Hand Guise Classification Based on Surface Electromyographic Signals Using Multichannel Convolutional Neural Network [0.0]
  Recent developments in the field of Machine Learning allow us to use EMG signals to teach machines the complex properties of human movements. Modern machines are capable of detecting numerous human activities and distinguishing among them solely based on the EMG signals produced by those activities. In this study, a novel classification method has been described employing a multichannel Convolutional Neural Network (CNN) that interprets surface EMG signals by the properties they exhibit in the power domain.
  arXiv  Detail & Related papers  (2021-01-17T17:02:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.