Markerless Motion Capture and Biomechanical Analysis Pipeline

• URL: http://arxiv.org/abs/2303.10654v1
• Date: Sun, 19 Mar 2023 13:31:57 GMT
• Title: Markerless Motion Capture and Biomechanical Analysis Pipeline
• Authors: R. James Cotton, Allison DeLillo, Anthony Cimorelli, Kunal Shah, J.D. Peiffer, Shawana Anarwala, Kayan Abdou, Tasos Karakostas
• Abstract summary: Markerless motion capture has the potential to expand access to precise movement analysis. Our pipeline makes it easy to obtain accurate biomechanical estimates of movement in a rehabilitation hospital.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing more sensitive tools for research. There are numerous steps between obtaining videos to extracting accurate biomechanical results and limited research to guide many critical design decisions in these pipelines. In this work, we analyze several of these steps including the algorithm used to detect keypoints and the keypoint set, the approach to reconstructing trajectories for biomechanical inverse kinematics and optimizing the IK process. Several features we find important are: 1) using a recent algorithm trained on many datasets that produces a dense set of biomechanically-motivated keypoints, 2) using an implicit representation to reconstruct smooth, anatomically constrained marker trajectories for IK, 3) iteratively optimizing the biomechanical model to match the dense markers, 4) appropriate regularization of the IK process. Our pipeline makes it easy to obtain accurate biomechanical estimates of movement in a rehabilitation hospital.

Related papers

• 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)
• Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture [2.44755919161855]
  Differentiable physics simulators can be accelerated on a GPU. We show that these simulators can be used to fit inverse kinematics to markerless motion capture data.
  arXiv  Detail & Related papers  (2024-02-27T04:18:15Z)
• EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration [49.90228618894857]
  We introduce a new approach to hand-eye calibration called EasyHeC, which is markerless, white-box, and delivers superior accuracy and robustness. We propose to use two key technologies: differentiable rendering-based camera pose optimization and consistency-based joint space exploration. Our evaluation demonstrates superior performance in synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-02T03:49:54Z)
• Improved Trajectory Reconstruction for Markerless Pose Estimation [0.0]
  Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views. We tested different keypoint detectors and reconstruction algorithms on markerless pose estimation accuracy. We found that using a top-down keypoint detector and reconstructing trajectories with an implicit function enabled accurate, smooth and anatomically plausible trajectories.
  arXiv  Detail & Related papers  (2023-03-04T13:16:02Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images [15.7026400415269]
  We propose a novel learning-to-learn framework for landmark detection. Our proposed framework is general and shows the potential to improve the efficiency of anatomical landmark detection.
  arXiv  Detail & Related papers  (2021-05-19T13:39:18Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer [10.369766652751169]
  Keypoint detection is an essential building block for many robotic applications. Deep learning methods have the ability to detect user-defined keypoints in a marker-less manner. We propose a new and autonomous way to define the keypoint locations that overcomes these challenges.
  arXiv  Detail & Related papers  (2020-10-15T22:38:37Z)
• Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI [15.686391154738006]
  We propose a novel method that can implicitly learn biomechanics-informed regularisation. The proposed method is validated in the context of myocardial motion tracking on 2D stacks of cardiac MRI data.
  arXiv  Detail & Related papers  (2020-06-08T16:29:13Z)

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.