WHO-Hand Hygiene Gesture Classification System
- URL: http://arxiv.org/abs/2110.02842v1
- Date: Wed, 6 Oct 2021 15:15:10 GMT
- Title: WHO-Hand Hygiene Gesture Classification System
- Authors: Rashmi Bakshi
- Abstract summary: More than one million cases of hospital-acquired infections occur in Europe annually.
Hand hygiene compliance may reduce the risk of transmission.
Future aim is to deploy a hand hygiene prediction system for healthcare workers in real-time.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The recent ongoing coronavirus pandemic highlights the importance of hand
hygiene practices in our daily lives, with governments and worldwide health
authorities promoting good hand hygiene practices. More than one million cases
of hospital-acquired infections occur in Europe annually. Hand hygiene
compliance may reduce the risk of transmission by reducing the number of
infections as well as healthcare expenditures. In this paper, the World Health
Organization, hand hygiene gestures are recorded and analyzed with the
construction of an aluminum frame, placed at the laboratory sink. The hand
hygiene gestures are recorded for thirty participants after conducting a
training session about hand hygiene gestures demonstration. The video
recordings are converted into image files and are organized into six different
hand hygiene classes. The Resnet50 framework selection for the classification
of multiclass hand hygiene stages. The model is trained with the first set of
classes; Fingers Interlaced, P2PFingers Interlaced, and Rotational Rub for 25
epochs. An accuracy of 44 percent for the first set of experiments with a loss
score greater than 1.5 in the validation set is achieved. The training steps
for the second set of classes; Rub hands palm to palm, Fingers Interlocked,
Thumb Rub are 50 epochs. An accuracy of 72 percent is achieved for the second
set with a loss score of less than 0.8 for the validation set. In this work, a
preliminary analysis of a robust hand hygiene dataset with transfer learning
takes place. The future aim for deploying a hand hygiene prediction system for
healthcare workers in real-time.
Related papers
- EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models [82.43729208063468]
Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety.<n>We study sycophancy -- models' tendency to uncritically echo user-provided information.<n>We introduce EchoBench, a benchmark to systematically evaluate sycophancy in medical LVLMs.
arXiv Detail & Related papers (2025-09-24T14:09:55Z) - Potion: Towards Poison Unlearning [47.00450933765504]
Adversarial attacks by malicious actors on machine learning systems pose significant risks.
The challenge in resolving such an attack arises in practice when only a subset of the poisoned data can be identified.
Our work addresses two key challenges to advance the state of the art in poison unlearning.
arXiv Detail & Related papers (2024-06-13T14:35:11Z) - Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals.
Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
arXiv Detail & Related papers (2024-04-26T16:39:50Z) - 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) - A Novel IoT-based Framework for Non-Invasive Human Hygiene Monitoring
using Machine Learning Techniques [1.4260605984981949]
This paper presents a novel framework for monitoring human hygiene using vibration sensors.
The approach is based on a combination of a geophone sensor, a digitizer, and a cost-efficient computer board in a practical enclosure.
Applying a Support Vector Machine for binary classification exhibits a promising accuracy of 95% in the classification of different hygiene habits.
arXiv Detail & Related papers (2022-07-07T18:48:48Z) - 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) - You Can Wash Hands Better: Accurate Daily Handwashing Assessment with Smartwatches [21.502362740250174]
We propose UWash, a wearable solution with smartwatches, to assess handwashing procedures.
We address the task of handwashing assessment from readings of motion sensors similar to the action segmentation problem in computer vision.
Experiments over 51 subjects show that UWash achieves an accuracy of 92.27% on handwashing gesture recognition.
arXiv Detail & Related papers (2021-12-09T12:23:06Z) - A Comparison of Deep Learning Models for the Prediction of Hand Hygiene
Videos [0.0]
This paper presents a comparison of various deep learning models such as Exception, Resnet-50, and Inception V3 for the classification and prediction of hand hygiene gestures.
The dataset consists of six hand hygiene movements in a video format, gathered for 30 participants.
An accuracy of 37% (Xception model), 33% (Inception V3), and 72% (ResNet-50) is achieved in the classification report after the training of the models for 25 epochs.
arXiv Detail & Related papers (2021-11-03T16:15:55Z) - Timely Tracking of Infection Status of Individuals in a Population [70.21702849459986]
We consider real-time timely tracking of infection status of individuals in a population.
In this work, a health care provider wants to detect infected people as well as people who recovered from the disease.
arXiv Detail & Related papers (2020-12-24T18:49:22Z) - Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US.
We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
arXiv Detail & Related papers (2020-06-23T03:36:19Z) - Predictive Modeling of ICU Healthcare-Associated Infections from
Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling
Approach [55.41644538483948]
This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units.
The aim is to support decision making addressed at reducing the incidence rate of infections.
arXiv Detail & Related papers (2020-05-07T16:13:12Z) - Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using
Deep Multiple-Instance Learning [59.74684475991192]
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old.
PD symptoms include tremor, rigidity and braykinesia.
We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
arXiv Detail & Related papers (2020-05-06T09:02:30Z)
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.