Automated Tracking of Hand Hygiene Stages

The European Centre for Disease Prevention and Control (ECDC) estimates that 2.5 millioncases of Hospital Acquired Infections (HAIs) occur each year in the European Union. Handhygiene is regarded as one of the most important preventive measures for HAIs. If it is implemented properly, hand hygiene can reduce the risk of cross-transmission of an infection in the healthcare environment. Good hand hygiene is not only important for healthcare settings. Therecent ongoing coronavirus pandemic has highlighted the importance of hand hygiene practices in our daily lives, with governments and health authorities around the world promoting goodhand hygiene practices. The WHO has published guidelines of hand hygiene stages to promotegood hand washing practices. A significant amount of existing research has focused on theproblem of tracking hands to enable hand gesture recognition. In this work, gesture trackingdevices and image processing are explored in the context of the hand washing environment.Hand washing videos of professional healthcare workers were carefully observed and analyzedin order to recognize hand features associated with hand hygiene stages that could be extractedautomatically. Selected hand features such as palm shape (flat or curved); palm orientation(palms facing or not); hand trajectory (linear or circular movement) were then extracted andtracked with the help of a 3D gesture tracking device - the Leap Motion Controller. These fea-tures were further coupled together to detect the execution of a required WHO - hand hygienestage,Rub hands palm to palm, with the help of the Leap sensor in real time. In certain conditions, the Leap Motion Controller enables a clear distinction to be made between the left andright hands. However, whenever the two hands came into contact with each other, sensor data from the Leap, such as palm position and palm orientation was lost for one of the two hands.Hand occlusion was found to be a major drawback with the application of the device to this usecase. Therefore, RGB digital cameras were selected for further processing and tracking of the hands. An image processing technique, using a skin detection algorithm, was applied to extractinstantaneous hand positions for further processing, to enable various hand hygiene poses to be detected. Contour and centroid detection algorithms were further applied to track the handtrajectory in hand hygiene video recordings. In addition, feature detection algorithms wereapplied to a hand hygiene pose to extract the useful hand features. The video recordings did not suffer from occlusion as is the case for the Leap sensor, but the segmentation of one handfrom another was identified as a major challenge with images because the contour detectionresulted in a continuous mass when the two hands were in contact. For future work, the datafrom gesture trackers, such as the Leap Motion Controller and cameras (with image processing)could be combined to make a robust hand hygiene gesture classification system

Identification of Unique Features and Exploration of Leap Motion Controller for Detecting Hand Hygiene Stages

Hospital acquired infections (HAIs) are contracted by the patients during the hospital stay. Antibiotic resistant microbes are the major cause and spread due to the contaminated medical equipment, crowded hospitals, frequent transfer of patients from one unit to another and poor hand hygiene. [1] In high income countries, approximately 30% of patients in intensive care units are affected by at least one HAI. [2] Hand hygiene is identified as a measure to prevent cross-transmission and to reduce the rate of HAI. [3] Technology is now used as a means of assessing hand hygiene compliance. Current approaches involve the use of electronic counters for measuring the number of hand washing events and product usage but the quality of hand wash is not taken into consideration. There are guidelines that demonstrate how to wash hands properly as per World Health Organisation (WHO). It is proposed that image and gesture based tracking devices may be suitable in assessing the quality of hand hygiene and adherence to accepted guidelines. In advance of implementing these approaches, this paper identifies and classifies the unique features (hand orientation and movement) for each stage of WHO guidelines. A 3D gesture tracking device (Leap Motion Controller) is then used to identify the stages based on this classification system

Feature Detection for Hand Hygiene Stages

The process of hand washing involves complex hand movements. There are six principal sequential steps for washing hands as per the World Health Organization (WHO) guidelines. In this work, a detailed description of an aluminum 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

Tracking Hand Trajectory as a Preliminary Study for Hand Hygiene Stages

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 preliminary analysis was undertaken in order to develop an automated image processing system for tracking and classification of two-handed dynamic gestures involved in hand washing. To facilitate this study, videos of healthcare workers who were engaged in washing hands were sourced from the internet. The videos were analysed in order to extract the unique features of two-handed gestures associated with all hand hygiene (HH) stages. The combination of these unique features can be used to detect each HH stage. In the video recordings, the hand trajectory was found to be linear or circular for all six HH stages. In this paper, we attempt to track hand trajectory with the help of 2.5 Mega Pixel ELP-USB cameras and using an image processing approach for skin detection and the contour-centroid detection method. The YCbCr colour space is invariant to illumination intensity and therefore it was selected for the skin detection method. This work concludes that cameras are suitable for tracking one hand movement- linear and circular motion as a preliminary work and can be further expanded for detecting two hand movements in hand washing

Feature Extraction, Classification and Prediction for Hand Hygiene Gestures with KNN Algorithm

There are six, well-structured hand gestures for washing hands as provided by World Health Organisation guidelines. In this paper, hand features such as contours of the hands, the centroid of the hands, and extreme hand points along the largest contour are extracted for specific hand-washing gestures with the use of a computer vision library, OpenCV. For this project, a robust dataset of hand hygiene video recordings is built with the help of 30 research participants. In this work, a subset of the dataset was used as a pilot study to demonstrate the effectiveness of the KNN algorithm. Extracted hand features saved in a CSV file are passed to a KNN model with a cross-fold validation technique for the classification and prediction of the unlabelled data. A mean accuracy score of >95% is achieved and proves that the KNN algorithm with an appropriate input value of K=3 is efficient for hand hygiene gestures classification

Sequential activation of Notch and Grainyhead gives apoptotic competence to Abdominal-B expressing larval neuroblasts in Drosophila Central nervous system.

Neural circuitry for mating and reproduction resides within the terminal segments of central nervous system (CNS) which express Hox paralogous group 9-13 (in vertebrates) or Abdominal-B (Abd-B) in Drosophila. Terminal neuroblasts (NBs) in A8-A10 segments of Drosophila larval CNS are subdivided into two groups based on expression of transcription factor Doublesex (Dsx). While the sex specific fate of Dsx-positive NBs is well investigated, the fate of Dsx-negative NBs is not known so far. Our studies with Dsx-negative NBs suggests that these cells, like their abdominal counterparts (in A3-A7 segments) use Hox, Grainyhead (Grh) and Notch to undergo cell death during larval development. This cell death also happens by transcriptionally activating RHG family of apoptotic genes through a common apoptotic enhancer in early to mid L3 stages. However, unlike abdominal NBs (in A3-A7 segments) which use increasing levels of resident Hox factor Abdominal-A (Abd-A) as an apoptosis trigger, Dsx-negative NBs (in A8-A10 segments) keep the levels of resident Hox factor Abd-B constant. These cells instead utilize increasing levels of the temporal transcription factor Grh and a rise in Notch activity to gain apoptotic competence. Biochemical and in vivo analysis suggest that Abdominal-A and Grh binding motifs in the common apoptotic enhancer also function as Abdominal-B and Grh binding motifs and maintains the enhancer activity in A8-A10 NBs. Finally, the deletion of this enhancer by the CRISPR-Cas9 method blocks the apoptosis of Dsx-negative NBs. These results highlight the fact that Hox dependent NB apoptosis in abdominal and terminal regions utilizes common molecular players (Hox, Grh and Notch), but seems to have evolved different molecular strategies to pattern CNS

Spontaneous Tumor Lysis Syndrome in Childhood T cell Acute Lymphoblastic Leukemia

Abstract We report two cases that presented with unexplained acute renal failure and hyperuricemia and were subsequently diagnosed with T-cell acute lymphoblastic leukemia. The patients improved with conservative therapy without the need for dialysis. Case 1 is the youngest case of acute lymphoblastic leukemia with spontaneous tumor lysis syndrome reported to date, and Case 2 presented with spontaneous tumor lysis syndrome without hyperleukocytosis

Investigation of community outbreak of enteric fever associated with drinking water in Borsad, Anand, Gujarat

Background:  Enteric fever is an endemic health problem and frequently associated with outbreaks. Objectives: Investigate sudden surge in gastroenteritis cases to confirm the outbreak, describe it in terms of time place and person, identify source of infection, modes of transmission and suggest remedial measures. Material & Methodology: Field visit was undertaken by the rapid response team (RRT) at Borsad town of Anand district in Gujarat for investigating suspected typhoid outbreak. This involved gathering information from local authorities, hospital admissions and home visits; collection of blood samples and water samples. Result:  Of 30 suspected cases, 19 (63.3%) tested Widal positive. Common presenting symptoms were fever, vomiting followed by diarrhea and abdominal pain. Chlorine levels at source and end users were inadequate. 5 leakage points in water supply were identified in areas around the clustered cases. Conclusion and Recommendations: Leakage in piped water supply coupled with inadequate chlorination lead to contaminated drinking water and subsequent Typhoid epidemic. Corrective steps in form of rapid surveys to identify cases, household chlorination and appropriate engineering measures were recommended and implemented