Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb
Prosthetic Users
- URL: http://arxiv.org/abs/2312.07854v1
- Date: Wed, 13 Dec 2023 02:48:11 GMT
- Title: Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb
Prosthetic Users
- Authors: Tianxun Zhou, Muhammad Nur Shahril Iskandar, Keng-Hwee Chiam
- Abstract summary: This study introduces an innovative zero-shot method employing image generation diffusion models to achieve markerless pose estimation for lower-limb prosthetics.
Our approach demonstrates an enhancement in detecting key points on prosthetic limbs over existing methods, and enables clinicians to gain invaluable insights into the kinematics of lower-limb amputees.
- Score: 3.686808512438363
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The application of 2D markerless gait analysis has garnered increasing
interest and application within clinical settings. However, its effectiveness
in the realm of lower-limb amputees has remained less than optimal. In
response, this study introduces an innovative zero-shot method employing image
generation diffusion models to achieve markerless pose estimation for
lower-limb prosthetics, presenting a promising solution to gait analysis for
this specific population. Our approach demonstrates an enhancement in detecting
key points on prosthetic limbs over existing methods, and enables clinicians to
gain invaluable insights into the kinematics of lower-limb amputees across the
gait cycle. The outcomes obtained not only serve as a proof-of-concept for the
feasibility of this zero-shot approach but also underscore its potential in
advancing rehabilitation through gait analysis for this unique population.
Related papers
- Validation of Human Pose Estimation and Human Mesh Recovery for Extracting Clinically Relevant Motion Data from Videos [79.62407455005561]
Marker-less motion capture using human pose estimation produces results in-line with the results of both the IMU and MoCap kinematics.
While there is still room for improvement when it comes to the quality of the data produced, we believe that this compromise is within the room of error.
arXiv Detail & Related papers (2025-03-18T22:18:33Z) - EyeBench: A Call for More Rigorous Evaluation of Retinal Image Enhancement [14.724629346280402]
generative models have achieved significant success in enhancement fundus images.
The evaluation of these models still presents a considerable challenge.
We propose a novel comprehensive benchmark, EyeBench, to provide insights that align enhancement models with clinical needs.
arXiv Detail & Related papers (2025-02-20T04:56:03Z) - Natias: Neuron Attribution based Transferable Image Adversarial Steganography [62.906821876314275]
adversarial steganography has garnered considerable attention due to its ability to effectively deceive deep-learning-based steganalysis.
We propose a novel adversarial steganographic scheme named Natias.
Our proposed method can be seamlessly integrated with existing adversarial steganography frameworks.
arXiv Detail & Related papers (2024-09-08T04:09:51Z) - Region Guided Attention Network for Retinal Vessel Segmentation [19.587662416331682]
We present a lightweight retinal vessel segmentation network based on the encoder-decoder mechanism with region-guided attention.
Dice loss penalises false positives and false negatives equally, encouraging the model to generate more accurate segmentation.
Experiments on a benchmark dataset show better performance (0.8285, 0.8098, 0.9677, and 0.8166 recall, precision, accuracy and F1 score respectively) compared to state-of-the-art methods.
arXiv Detail & Related papers (2024-07-22T00:08:18Z) - A marker-less human motion analysis system for motion-based biomarker
discovery in knee disorders [60.99112047564336]
The NHS has been having increased difficulty seeing all low-risk patients, this includes but not limited to suspected osteoarthritis (OA) patients.
We propose a novel method of automated biomarker identification for diagnosis of knee disorders and the monitoring of treatment progression.
arXiv Detail & Related papers (2023-04-26T16:47:42Z) - 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) - Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion
Segmentation [8.668715385199889]
We present a few-shot segmentation approach for ischemic lesion segmentation using only one annotated sample during training.
We exploit color-coded parametric maps generated from Computed Tomography Perfusion scans.
Given a single annotated patient, an average Dice score of 0.58 is achieved for the segmentation of ischemic lesions.
arXiv Detail & Related papers (2023-03-02T15:10:08Z) - Rethinking Semi-Supervised Medical Image Segmentation: A
Variance-Reduction Perspective [51.70661197256033]
We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation.
We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks.
We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
arXiv Detail & Related papers (2023-02-03T13:50:25Z) - Markerless Suture Needle 6D Pose Tracking with Robust Uncertainty
Estimation for Autonomous Minimally Invasive Robotic Surgery [11.530352384883361]
We present a novel approach for markerless suture needle pose tracking using Bayesian filters.
A data-efficient feature point detector is trained to extract the feature points on the needle.
A novel observation model measures the overlap between the detections and the expected projection of the needle.
arXiv Detail & Related papers (2021-09-26T23:30:14Z) - On the Robustness of Pretraining and Self-Supervision for a Deep
Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
We compare the impact of different training procedures for diabetic retinopathy grading.
We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions.
Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
arXiv Detail & Related papers (2021-06-25T08:32:45Z) - Geodesic B-Score for Improved Assessment of Knee Osteoarthritis [0.13221754103523226]
Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status.
There remains a vast need for reader-independent measures that provide reliable assessment of subject-specific clinical outcomes.
arXiv Detail & Related papers (2021-03-12T12:16:21Z) - One-shot action recognition towards novel assistive therapies [63.23654147345168]
This work is motivated by the automated analysis of medical therapies that involve action imitation games.
The presented approach incorporates a pre-processing step that standardizes heterogeneous motion data conditions.
We evaluate the approach on a real use-case of automated video analysis for therapy support with autistic people.
arXiv Detail & Related papers (2021-02-17T19:41:37Z) - Explaining Clinical Decision Support Systems in Medical Imaging using
Cycle-Consistent Activation Maximization [112.2628296775395]
Clinical decision support using deep neural networks has become a topic of steadily growing interest.
clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend.
We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
arXiv Detail & Related papers (2020-10-09T14:39:27Z)
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.