Use of thermal imaging to measure the quality of hand hygiene

OBJECTIVES

Hand hygiene has long been promoted as the most effective way to prevent the transmission of infection. However, due to low compliance and low quality of hand hygiene reported in previous studies, constant monitoring of hand hygiene compliance and quality among healthcare workers is crucial. This study investigated the feasibility of using a thermal camera with an RGB camera to detect hand coverage of alcohol-based formulation, thereby monitoring the quality of hand rubbing.

METHODS

In total, 32 participants were recruited to participate in this study. Participants were required to perform four types of hand rubbing to achieve different coverage of the alcohol-based formulation. After each task, participants' hands were photographed under a thermal camera and an RGB camera, while an ultraviolet (UV) test was used to provide the ground truth of hand coverage of alcohol-based formulation. U-Net was used to segment areas exposed to alcohol-based formulation from thermal images, and system performance was evaluated by comparing differences in coverage between thermal images and UV images in terms of accuracy and Dice coefficient.

RESULTS

This system found promising results in terms of accuracy (93.5%) and Dice coefficient (87.1%) when observations took place 10 s after hand rubbing. At 60 s after hand rubbing, accuracy and Dice coefficient were 92.4% and 85.7%.

CONCLUSIONS

Thermal imaging has potential for accurate, constant and systematic monitoring of the quality of hand hygiene.

Fully 3D-Printed Dry EEG Electrodes

Electroencephalography (EEG) is used to detect brain activity by recording electrical signals across various points on the scalp. Recent technological advancement has allowed brain signals to be monitored continuously through the long-term usage of EEG wearables. However, current EEG electrodes are not able to cater to different anatomical features, lifestyles, and personal preferences, suggesting the need for customisable electrodes. Despite previous efforts to create customisable EEG electrodes through 3D printing, additional processing after printing is often needed to achieve the required electrical properties. Although fabricating EEG electrodes entirely through 3D printing with a conductive material would eliminate the need for further processing, fully 3D-printed EEG electrodes have not been seen in previous studies. In this study, we investigate the feasibility of using a low-cost setup and a conductive filament, Multi3D Electrifi, to 3D print EEG electrodes. Our results show that the contact impedance between the printed electrodes and an artificial phantom scalp is under 550 Ω, with phase change of smaller than -30∘, for all design configurations for frequencies ranging from 20 Hz to 10 kHz. In addition, the difference in contact impedance between electrodes with different numbers of pins is under 200 Ω for all test frequencies. Through a preliminary functional test that monitored the alpha signals (7-13 Hz) of a participant in eye-open and eye-closed states, we show that alpha activity can be identified using the printed electrodes. This work demonstrates that fully 3D-printed electrodes have the capability of acquiring relatively high-quality EEG signals.

A System for Computational Assessment of Hand Hygiene Techniques

The World Health Organization (WHO) recommends a six-step hand hygiene technique. Although multiple studies have reported that this technique yields inadequate skin coverage outcomes, they have relied on manual labeling that provided low-resolution estimations of skin coverage outcomes. We have developed a computational system to precisely quantify hand hygiene outcomes and provide high-resolution skin coverage visualizations, thereby improving hygiene techniques. We identified frequently untreated areas located at the dorsal side of the hands around the abductor digiti minimi and the first dorsal interosseous. We also estimated that excluding Steps 3, 6R, and 6L from the six-step hand hygiene technique leads to cumulative coverage loss of less than 1%, indicating the potential redundancy of these steps. Our study demonstrates that the six-step hand hygiene technique could be improved to reduce the untreated areas and remove potentially redundant steps. Furthermore, our system can be used to computationally validate new proposed techniques, and help optimise hand hygiene procedures.

Electronic Monitoring Systems for Hand Hygiene: Systematic Review of Technology

BACKGROUND

Hand hygiene is one of the most effective ways of preventing health care-associated infections and reducing their transmission. Owing to recent advances in sensing technologies, electronic hand hygiene monitoring systems have been integrated into the daily routines of health care workers to measure their hand hygiene compliance and quality.

OBJECTIVE

This review aims to summarize the latest technologies adopted in electronic hand hygiene monitoring systems and discuss the capabilities and limitations of these systems.

METHODS

A systematic search of PubMed, ACM Digital Library, and IEEE Xplore Digital Library was performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were initially screened and assessed independently by the 2 authors, and disagreements between them were further summarized and resolved by discussion with the senior author.

RESULTS

In total, 1035 publications were retrieved by the search queries; of the 1035 papers, 89 (8.60%) fulfilled the eligibility criteria and were retained for review. In summary, 73 studies used electronic monitoring systems to monitor hand hygiene compliance, including application-assisted direct observation (5/73, 7%), camera-assisted observation (10/73, 14%), sensor-assisted observation (29/73, 40%), and real-time locating system (32/73, 44%). A total of 21 studies evaluated hand hygiene quality, consisting of compliance with the World Health Organization 6-step hand hygiene techniques (14/21, 67%) and surface coverage or illumination reduction of fluorescent substances (7/21, 33%).

CONCLUSIONS

Electronic hand hygiene monitoring systems face issues of accuracy, data integration, privacy and confidentiality, usability, associated costs, and infrastructure improvements. Moreover, this review found that standardized measurement tools to evaluate system performance are lacking; thus, future research is needed to establish standardized metrics to measure system performance differences among electronic hand hygiene monitoring systems. Furthermore, with sensing technologies and algorithms continually advancing, more research is needed on their implementation to improve system performance and address other hand hygiene-related issues.

Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study

Background

Hand hygiene is a crucial and cost-effective method to prevent health care–associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation.

Objective

We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing.

Methods

We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping.

Results

Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance.

Conclusions

A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands.

Assisted Medication Management in Elderly Care Using Miniaturised Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIRS) measures the light reflected from objects to infer highly detailed information about their molecular composition. Traditionally, NIRS has been an instrument reserved for laboratory usage, but recently affordable and smaller devices for NIRS have proliferated. Pairing this technology with the ubiquitous smartphone opens up a plethora of new use cases. In this paper, we explore one such use case, namely medication management in a nursing home/elderly care centre. First, we conducted a qualitative user study with nurses working in an elderly care centre to examine the protocols and workflows involved in administering medication, and the nurses' perceptions on using this technology. Based on our findings, we identify the main impact areas that would benefit from introducing miniaturised NIRS. Finally, we demonstrate via a user study in a realistic scenario that miniaturised NIRS can be effectively used for medication management when leveraging appropriate machine learning techniques. Specifically, we assess the performance of multiple pre-processing and classification algorithms for a selected set of pharmaceuticals. In addition, we compare our solution with currently used methods for pharmaceutical identification in a local care centre. We hope that our reflection on the multiple aspects associated with the introduction of this device in an elderly care setting can help both academics and practitioners working on related problems.