The bionic neural network for external simulation of human locomotor system

• URL: http://arxiv.org/abs/2309.05863v1
• Date: Mon, 11 Sep 2023 23:02:56 GMT
• Title: The bionic neural network for external simulation of human locomotor system
• Authors: Yue Shi, Shuhao Ma, Yihui Zhao
• Abstract summary: This paper proposes a physics-informed deep learning method based on musculoskeletal (MSK) modeling to predict joint motion and muscle forces. The method can effectively identify subject-specific MSK physiological parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion and muscle forces predictions.
• Score: 2.6311880922890842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Muscle forces and joint kinematics estimated with musculoskeletal (MSK) modeling techniques offer useful metrics describing movement quality. Model-based computational MSK models can interpret the dynamic interaction between the neural drive to muscles, muscle dynamics, body and joint kinematics, and kinetics. Still, such a set of solutions suffers from high computational time and muscle recruitment problems, especially in complex modeling. In recent years, data-driven methods have emerged as a promising alternative due to the benefits of flexibility and adaptability. However, a large amount of labeled training data is not easy to be acquired. This paper proposes a physics-informed deep learning method based on MSK modeling to predict joint motion and muscle forces. The MSK model is embedded into the neural network as an ordinary differential equation (ODE) loss function with physiological parameters of muscle activation dynamics and muscle contraction dynamics to be identified. These parameters are automatically estimated during the training process which guides the prediction of muscle forces combined with the MSK forward dynamics model. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The results demonstrate that the proposed deep learning method can effectively identify subject-specific MSK physiological parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion and muscle forces predictions.

Related papers

• Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations [64.98299559470503]
  Muscles in Time (MinT) is a large-scale synthetic muscle activation dataset. It contains over nine hours of simulation data covering 227 subjects and 402 simulated muscle strands. We show results on neural network-based muscle activation estimation from human pose sequences.
  arXiv  Detail & Related papers  (2024-10-31T18:28:53Z)
• MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints [50.61346764110482]
  We integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create MS-MANO. This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories. We also propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron network.
  arXiv  Detail & Related papers  (2024-04-16T02:18:18Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [74.93549765488103]
  In drug discovery, molecular dynamics simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding. We show the efficiency and effectiveness of NeuralMD, with a 2000$times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network [0.0]
  The PINN model is constructed by combining a feed-forward Artificial Neural Network (ANN) with a joint torque model. The training dataset for the PINN model comprises EMG and time data collected from four different subjects. The results demonstrated strong correlations of 58% to 83% in joint angle prediction.
  arXiv  Detail & Related papers  (2023-11-28T16:55:11Z)
• 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 Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems [1.978587235008588]
  This work presents a physics-informed recurrent neural network (MR PI-RNN) for simultaneous prediction of musculoskeletal (MSK) motion. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The framework is also capable of identifying muscle parameters that are physiologically consistent with the subject's kinematics data.
  arXiv  Detail & Related papers  (2023-05-26T02:51:39Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics [0.0]
  We propose an interpretation of neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models (HMs) This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. We produce a new tool for determining important communities of brain regions using a random walk-based procedure.
  arXiv  Detail & Related papers  (2022-03-17T12:14:09Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• 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)
• Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE [10.529943544385585]
  We propose a method that integrates key ingredients from latent models and traditional neural encoding models. Our method, pi-VAE, is inspired by recent progress on identifiable variational auto-encoder. We validate pi-VAE using synthetic data, and apply it to analyze neurophysiological datasets from rat hippocampus and macaque motor cortex.
  arXiv  Detail & Related papers  (2020-11-09T22:00:38Z)

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.