Feature Detection for Hand Hygiene Stages
- URL: http://arxiv.org/abs/2108.03015v1
- Date: Fri, 6 Aug 2021 09:21:03 GMT
- Title: Feature Detection for Hand Hygiene Stages
- Authors: Rashmi Bakshi, Jane Courtney, Damon Berry, Graham Gavin
- Abstract summary: There are six principal sequential steps for washing hands as per the World Health Organisation (WHO) guidelines.
In this work, a detailed description of an aluminium rig construction for creating a robust hand-washing dataset is discussed.
Preliminary results with the help of image processing and computer vision algorithms for hand pose extraction and feature detection are demonstrated.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The process of hand washing involves complex hand movements. There are six
principal sequential steps for washing hands as per the World Health
Organisation (WHO) guidelines. In this work, a detailed description of an
aluminium rig construction for creating a robust hand-washing dataset is
discussed. The preliminary results with the help of image processing and
computer vision algorithms for hand pose extraction and feature detection such
as Harris detector, Shi-Tomasi and SIFT are demonstrated. The hand hygiene
pose- Rub hands palm to palm was captured as an input image for running all the
experiments. The future work will focus upon processing the video recordings of
hand movements captured and applying deep-learning solutions for the
classification of hand-hygiene stages.
Related papers
- BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects [70.20706475051347]
BimArt is a novel generative approach for synthesizing 3D bimanual hand interactions with articulated objects.
We first generate distance-based contact maps conditioned on the object trajectory with an articulation-aware feature representation.
The learned contact prior is then used to guide our hand motion generator, producing diverse and realistic bimanual motions for object movement and articulation.
arXiv Detail & Related papers (2024-12-06T14:23:56Z) - HandRefiner: Refining Malformed Hands in Generated Images by Diffusion-based Conditional Inpainting [72.95232302438207]
Diffusion models have achieved remarkable success in generating realistic images.
But they suffer from generating accurate human hands, such as incorrect finger counts or irregular shapes.
This paper introduces a lightweight post-processing solution called HandRefiner.
arXiv Detail & Related papers (2023-11-29T08:52:08Z) - GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency [57.9920824261925]
Hands are dexterous and highly versatile manipulators that are central to how humans interact with objects and their environment.
modeling realistic hand-object interactions is critical for applications in computer graphics, computer vision, and mixed reality.
GRIP is a learning-based method that takes as input the 3D motion of the body and the object, and synthesizes realistic motion for both hands before, during, and after object interaction.
arXiv Detail & Related papers (2023-08-22T17:59:51Z) - Hand Hygiene Assessment via Joint Step Segmentation and Key Action
Scorer [32.29065180155029]
Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO)
We propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment.
Under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations.
arXiv Detail & Related papers (2022-09-25T13:47:21Z) - 3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal [85.30756038989057]
Estimating 3D interacting hand pose from a single RGB image is essential for understanding human actions.
We propose to decompose the challenging interacting hand pose estimation task and estimate the pose of each hand separately.
Experiments show that the proposed method significantly outperforms previous state-of-the-art interacting hand pose estimation approaches.
arXiv Detail & Related papers (2022-07-22T13:04:06Z) - Feature Extraction and Prediction for Hand Hygiene Gestures with KNN
Algorithm [0.0]
This work focuses upon the analysis of hand gestures involved in the process of hand washing.
Hand features such as contours of hands, the centroid of the hands, and extreme hand points along the largest contour are extracted.
arXiv Detail & Related papers (2021-12-30T14:56:07Z) - A Deep Learning Based Automated Hand Hygiene Training System [0.12313056815753944]
WHO recommends a guideline for alcohol-based hand rub in eight steps to ensure that all surfaces of hands are entirely clean.
Deep Neural Network (DNN) and machine vision have made it possible to accurately evaluate hand rubbing quality.
In this paper, an automated deep learning based hand rub assessment system with real-time feedback is presented.
arXiv Detail & Related papers (2021-12-10T17:01:44Z) - Hand Hygiene Video Classification Based on Deep Learning [0.0]
A subset of robust dataset that consist of handwashing gestures with two hands as well as one-hand gestures utilized.
A pretrained neural network model, RES Net 50, with image net weights used for the classification of 3 categories: Linear hand movement, rub hands palm to palm and rub hands with fingers interlaced movement.
arXiv Detail & Related papers (2021-08-18T12:56:07Z) - Tracking Hand Hygiene Gestures with Leap Motion Controller [0.0]
The process of hand washing, according to the WHO, is divided into stages with clearly defined two handed dynamic gestures.
In this paper, videos of hand washing experts are segmented and analyzed with the goal of extracting their corresponding features.
A 3D gesture tracker, the Leap Motion Controller (LEAP), was used to track and detect the hand features associated with these stages.
arXiv Detail & Related papers (2021-08-11T08:48:39Z) - Learning to Disambiguate Strongly Interacting Hands via Probabilistic
Per-pixel Part Segmentation [84.28064034301445]
Self-similarity, and the resulting ambiguities in assigning pixel observations to the respective hands, is a major cause of the final 3D pose error.
We propose DIGIT, a novel method for estimating the 3D poses of two interacting hands from a single monocular image.
We experimentally show that the proposed approach achieves new state-of-the-art performance on the InterHand2.6M dataset.
arXiv Detail & Related papers (2021-07-01T13:28:02Z) - Physics-Based Dexterous Manipulations with Estimated Hand Poses and
Residual Reinforcement Learning [52.37106940303246]
We learn a model that maps noisy input hand poses to target virtual poses.
The agent is trained in a residual setting by using a model-free hybrid RL+IL approach.
We test our framework in two applications that use hand pose estimates for dexterous manipulations: hand-object interactions in VR and hand-object motion reconstruction in-the-wild.
arXiv Detail & Related papers (2020-08-07T17:34:28Z) - Body2Hands: Learning to Infer 3D Hands from Conversational Gesture Body
Dynamics [87.17505994436308]
We build upon the insight that body motion and hand gestures are strongly correlated in non-verbal communication settings.
We formulate the learning of this prior as a prediction task of 3D hand shape over time given body motion input alone.
Our hand prediction model produces convincing 3D hand gestures given only the 3D motion of the speaker's arms as input.
arXiv Detail & Related papers (2020-07-23T22:58:15Z)
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.