Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors

• URL: http://arxiv.org/abs/2603.16233v1
• Date: Tue, 17 Mar 2026 08:20:39 GMT
• Title: Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors
• Abstract summary: Ground Reaction Inertial Poser (GRIP) is a method that reconstructs physically plausible human motion using four wearable devices.<n>Unlike conventional IMU-only approaches, GRIP combines IMU signals with foot pressure data to capture both body dynamics and ground interactions.
• Score: 39.7490141561719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose Ground Reaction Inertial Poser (GRIP), a method that reconstructs physically plausible human motion using four wearable devices. Unlike conventional IMU-only approaches, GRIP combines IMU signals with foot pressure data to capture both body dynamics and ground interactions. Furthermore, rather than relying solely on kinematic estimation, GRIP uses a digital twin of a person, in the form of a synthetic humanoid in a physics simulator, to reconstruct realistic and physically plausible motion. At its core, GRIP consists of two modules: KinematicsNet, which estimates body poses and velocities from sensor data, and DynamicsNet, which controls the humanoid in the simulator using the residual between the KinematicsNet prediction and the simulated humanoid state. To enable robust training and fair evaluation, we introduce a large-scale dataset, Pressure and Inertial Sensing for Human Motion and Interaction (PRISM), that captures diverse human motions with synchronized IMUs and insole pressure sensors. Experimental results show that GRIP outperforms existing IMU-only and IMU-pressure fusion methods across all evaluated datasets, achieving higher global pose accuracy and improved physical consistency.

Related papers

• Physically Accurate Rigid-Body Dynamics in Particle-Based Simulation [38.44669833143752]
  Particle-based simulators offer a compelling alternative to current simulators.<n>Position-based dynamics (PBD) is a popular solver known for its computational efficiency and visual plausibility.<n>We introduce PBD-R, a revised PBD formulation that enforces physically accurate rigid-body dynamics.
  arXiv  Detail & Related papers  (2026-03-15T22:09:42Z)
• PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization [55.25800735159481]
  Whole-Body Controller (WBC) converts diffusion-generated motions into executable trajectories.<n>WBC trajectories become compliant with physics, but they may expose substantial deviations from original motion.<n>We propose PhysMoDPO, a Direct Preference Optimization framework.<n> PhysMoDPO results in significant improvements when applied to zero-shot motion transfer in simulation and for real-world deployment on a G1 humanoid robot.
  arXiv  Detail & Related papers  (2026-03-13T17:59:59Z)
• ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning [59.64325421657381]
  Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks.<n>We introduce ResMimic, a two-stage residual learning framework for precise and expressive humanoid control from human motion data.<n>Results show substantial gains in task success, training efficiency, and robustness over strong baselines.
  arXiv  Detail & Related papers  (2025-10-06T17:47:02Z)
• Physics-informed Ground Reaction Dynamics from Human Motion Capture [4.4795626402834055]
  We propose a novel method for estimating human ground reaction dynamics directly from motion capture data.<n>We introduce a highly accurate and robust method for computing ground reaction forces from motion capture data using Euler's integration scheme and PD algorithm.<n>The proposed approach was tested on the GroundLink dataset.
  arXiv  Detail & Related papers  (2025-07-02T04:02:16Z)
• Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models [25.20942802233326]
  Garment Inertial Poser (GaIP) is a method for estimating full-body poses from sparse and loosely attached IMU sensors.<n>Our experiments show that our diffusion methods trained on simulated and synthetic data outperform state-of-the-art inertial full-body pose estimators.
  arXiv  Detail & Related papers  (2025-06-18T09:16:36Z)
• 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)
• Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors [17.3834029178939]
  This paper introduces a novel human pose estimation approach using sparse inertial sensors. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. The approach demonstrates superior performance over state-of-the-art models across five public datasets, notably reducing pose error by 19% on the DIP-IMU dataset.
  arXiv  Detail & Related papers  (2023-12-02T13:17:10Z)
• 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)
• Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps [100.72245315180433]
  We present a reconfigurable data glove design to capture different modes of human hand-object interactions. The glove operates in three modes for various downstream tasks with distinct features. We evaluate the system's three modes by (i) recording hand gestures and associated forces, (ii) improving manipulation fluency in VR, and (iii) producing realistic simulation effects of various tool uses.
  arXiv  Detail & Related papers  (2023-01-14T05:35:50Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• Transformer Inertial Poser: Attention-based Real-time Human Motion Reconstruction from Sparse IMUs [79.72586714047199]
  We propose an attention-based deep learning method to reconstruct full-body motion from six IMU sensors in real-time. Our method achieves new state-of-the-art results both quantitatively and qualitatively, while being simple to implement and smaller in size.
  arXiv  Detail & Related papers  (2022-03-29T16:24:52Z)

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.