Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics

• URL: http://arxiv.org/abs/2511.10878v1
• Date: Fri, 14 Nov 2025 01:15:10 GMT
• Title: Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics
• Authors: Shuhao Ma, Zeyi Huang, Yu Cao, Wesley Doorsamy, Chaoyang Shi, Jun Li, Zhi-Qiang Zhang,
• Abstract summary: Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control.<n>We propose a physics-informed deep learning framework that estimates muscle activations and forces directly from kinematics.<n>We show that PI-MJCA-BiGRU achieves performance comparable to conventional supervised methods without requiring ground-truth labels.
• Score: 14.935995015069487
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control. However, conventional approaches are computationally expensive and lack a high-quality labeled dataset for multi-joint applications. To address these challenges, we propose a physics-informed deep learning framework that estimates muscle activations and forces directly from kinematics. The framework employs a novel Multi-Joint Cross-Attention (MJCA) module with Bidirectional Gated Recurrent Unit (BiGRU) layers to capture inter-joint coordination, enabling each joint to adaptively integrate motion information from others. By embedding multi-joint dynamics, inter-joint coupling, and external force interactions into the loss function, our Physics-Informed MJCA-BiGRU (PI-MJCA-BiGRU) delivers physiologically consistent predictions without labeled data while enabling time-efficient inference. Experimental validation on two datasets demonstrates that PI-MJCA-BiGRU achieves performance comparable to conventional supervised methods without requiring ground-truth labels, while the MJCA module significantly enhances inter-joint coordination modeling compared to other baseline architectures.

Related papers

• FAIM: Frequency-Aware Interactive Mamba for Time Series Classification [87.84511960413715]
  Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition.<n>We propose FAIM, a lightweight Frequency-Aware Interactive Mamba model.<n>We show that FAIM consistently outperforms existing state-of-the-art (SOTA) methods, achieving a superior trade-off between accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-11-26T08:36:33Z)
• Foundation Model for Skeleton-Based Human Action Understanding [56.89025287217221]
  This paper presents a Unified Skeleton-based Dense Representation Learning framework.<n>USDRL consists of a Transformer-based Dense Spatio-Temporal (DSTE), Multi-Grained Feature Decorrelation (MG-FD), and Multi-Perspective Consistency Training (MPCT)
  arXiv  Detail & Related papers  (2025-08-18T02:42:16Z)
• Zero-Shot EEG-to-Gait Decoding via Phase-Aware Representation Learning [9.49131859415923]
  We propose NeuroDyGait, a domain-generalizable EEG-to-motion decoding framework.<n>It uses structured contrastive representation learning and relational domain modeling to achieve semantic alignment between EEG and motion embeddings.<n>It achieves zero-shot motion prediction for unseen individuals without requiring adaptation and superior performance in cross-subject gait decoding on benchmark datasets.
  arXiv  Detail & Related papers  (2025-06-24T06:03:49Z)
• A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening [11.791358860917189]
  deep learning methods for remote sensing pansharpening have advanced rapidly.<n>Many existing methods struggle to fully leverage feature heterogeneity and redundancy.<n>We introduce a general adaptive dual-level weighting mechanism (ADWM) to address these challenges.
  arXiv  Detail & Related papers  (2025-03-17T14:24:00Z)
• DynSTG-Mamba: Dynamic Spatio-Temporal Graph Mamba with Cross-Graph Knowledge Distillation for Gait Disorders Recognition [0.3517488417793045]
  DynTG-Mamba is a novel framework that combines DF-STGNN and STG-Mamba to enhance motion modeling.<n> DF-STGNN incorporates a dynamic spatial filter that adaptively adjusts between skeletal joints and temporal interactions.<n> STG-Mamba, an extension of Mamba, ensures a continuous propagation of states while reducing computational costs.
  arXiv  Detail & Related papers  (2025-03-17T13:26:47Z)
• sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics [5.524068837259551]
  Exoskeletons and rehabilitation systems have the potential to improve human strength and recovery by using adaptive human-machine interfaces.<n>We introduce the Physics-informed Gated Recurrent Network (PiGRN), a novel model designed to predict multi-joint movement dynamics from sEMG data.<n>PiGRN uses a Gated Recurrent Unit (GRU) to process time-series sEMG inputs, estimate multi-joint kinematics and external loads, and predict joint torque while incorporating physics-based constraints during training.
  arXiv  Detail & Related papers  (2024-08-29T15:09:04Z)
• Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders [0.0]
  We introduce Temporal Autoencoders for Causal Inference (TACI) TACI combines a new surrogate data metric for assessing causal interactions with a novel two-headed machine learning architecture. We demonstrate TACI's ability to accurately quantify dynamic causal interactions across a variety of systems.
  arXiv  Detail & Related papers  (2024-06-05T12:51:20Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets [53.367212596352324]
  We propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field, source-free domain adaptation. Our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications.
  arXiv  Detail & Related papers  (2024-03-07T16:17:33Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• SpatioTemporal Focus for Skeleton-based Action Recognition [66.8571926307011]
  Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information.
  arXiv  Detail & Related papers  (2022-03-31T02:45:24Z)

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.