Personal Protective Equipment Compliance Research Articles

Impact of Aircraft Noise and Compliance with the Use of Personal Protective Equipment on Hearing Impairment in Pilot Cadets at Indonesian Civil Pilot Academy - Banyuwangi

Noise in the workplace is a health issue in several countries. Similarly, in the aerospace industry, advanced aircraft, whether for civilian transportation or military purposes, pose risks due to exposure to aircraft noise, which passengers are continuously subjected to at relatively higher intensities and for prolonged periods. The problem of this research is to identify, reduce, and prevent the impact of aircraft noise and compliance with the use of personal protective equipment (PPE) on hearing impairment among cadet pilots at the Indonesian Aviation Academy in Banyuwangi. The research objective includes collecting medical records of 57 cadets, measuring aircraft noise levels, and conducting a questionnaire survey. The method used in this study is descriptive quantitative, with 57 respondents. Data analysis was performed to determine the compliance of cadets with PPE usage at the Indonesian Aviation Academy in Banyuwangi. The results show that the noise produced by the aircraft operated by the academy exceeds the permissible noise level (PNL), and the questionnaire results indicate low compliance with PPE usage. However, medical record data reveal that the majority of cadets still have normal hearing, with only 12.3% or 7 respondents categorized as having mild hearing impairment. These findings imply a positive and significant influence of aircraft noise level and PPE compliance on hearing impairment among trainees.

Deep Learning for Detection of Proper Utilization and Adequacy of Personal Protective Equipment in Manufacturing Teaching Laboratories

Occupational sectors are perennially challenged by the potential for workplace accidents, particularly in roles involving tools and machinery. A notable cause of such accidents is the inadequate use of Personal Protective Equipment (PPE), essential in preventing injuries and illnesses. This risk is not confined to workplaces alone but extends to educational settings with practical activities, like manufacturing teaching laboratories in universities. Current methods for monitoring and ensuring proper PPE usage especially in the laboratories are limited, lacking in real-time and accurate detection capabilities. This study addresses this gap by developing a visual-based, deep learning system specifically tailored for assessing PPE usage in manufacturing teaching laboratories. The method of choice for object detection in this study is You Only Look Once (YOLO) algorithms, encompassing YOLOv4, YOLOv5, and YOLOv6. YOLO processes images in a single pass through its architecture, in which its efficiency allows for real-time detection. The novel contribution of this study lies in its computer vision models, adept at not only detecting compliance but also assessing adequacy of PPE usage. The result indicates that the proposed computer vision models achieve high accuracy for detection of PPE usage compliance and adequacy with a mAP value of 0.757 and an F1-score of 0.744, obtained with the YOLOv5 model. The implementation of a deep learning system for PPE compliance in manufacturing teaching laboratories could markedly improve safety, preventing accidents and injuries through real-time compliance monitoring. Its effectiveness and adaptability could set a precedent for safety protocols in various educational settings, fostering a wider culture of safety and compliance.

Object Detection for Safety Attire Using YOLO (You Only Look Once)

Personal protective equipment (PPE) usage is mandated for all employees to prevent workplace accidents and foster a safe and healthy work environment. Using YOLOv8 machine learning and Google Colab's web-based development environment, this research aims to create an immediate detection system for PPE violations in the workplace. By keeping track of PPE compliance, the system is intended to increase workplace safety and prevent accidents. The dataset is collected through a mixture of real-life image gathering and internet datasets. Various images are collected that aim to train the model to detect objects from afar, close, and individually. The research methodology includes a review of the literature, the gathering, pre-processing, and training of models. According to the use of safety helmets, safety shoes, and gloves, there are three different classes of detection based on the bounding box. The system successfully detected the classes with an overall score above 0.8. The safety helmet achieved 0.969, the safety gloves achieved 0.857, followed by the safety vest with 0.887. The findings from this study indicate that the developed system can effectively improve occupational safety and health management. However, there is a detection error factor caused by the lighting and colors. Future research can focus on integrating the system with other work safety systems to provide a comprehensive solution for accident prevention.

Enhancing Workplace Safety: PPE_Swin—A Robust Swin Transformer Approach for Automated Personal Protective Equipment Detection

Accidents occur in the construction industry as a result of non-compliance with personal protective equipment (PPE). As a result of diverse environments, it is difficult to detect PPE automatically. Traditional image detection models like convolutional neural network (CNN) and vision transformer (ViT) struggle to capture both local and global features in construction safety. This study introduces a new approach for automating the detection of personal protective equipment (PPE) in the construction industry, called PPE_Swin. By combining global and local feature extraction using the self-attention mechanism based on Swin-Unet, we address challenges related to accurate segmentation, robustness to image variations, and generalization across different environments. In order to train and evaluate our system, we have compiled a new dataset, which provides more reliable and accurate detection of personal protective equipment (PPE) in diverse construction scenarios. Our approach achieves a remarkable 97% accuracy in detecting workers with and without PPE, surpassing existing state-of-the-art methods. This research presents an effective solution for enhancing worker safety on construction sites by automating PPE compliance detection.

Gender aspects in occupational exposure and health studies.

While sex is a biological attribute associated with physical and physiological features, gender refers to socially constructed roles, behaviors, expressions, and identities. On the biological side, males and females differ concerning hormonal and anatomical differences, and It is therefore plausible occupational exposure may act differently on males and females. In contrast, gender may influence the work organization, work environment conditions and exposures, and employment conditions, leading people of different genders to perform different jobs or job tasks, be exposed to different stressors, and work under different employment terms. Traditionally, occupational exposures have been assessed without considering how exposures may differ by sex or gender. Early research focused on occupations that primarily employed men. However, women have entered occupations historically dominated by men, leading to emerging exploration of gender differences in exposure. Some women-dominated occupations have become the focus of intensive research activity. In the Annals, the number of articles including sex and gender issues has increased dramatically over time, with only two published prior to 1980, and 70 in the 2010s, and with a special issue dedicated to Gender, Work, and Health in 2018 where the editors highlighted a need to improve assessment of gender and sex identities to allow for more nuanced knowledge to elucidate the role of work organization and contextual factors about gender, work exposures, and health. Females, on average, have different body dimensions than males, which affects how well workplaces and personal protective equipment (PPE) fit females, and there remains a need for further improvements to ensure that females are protected equally well. On the other hand, females tend to comply more frequently with PPE requirements than men highlighting the need for gender-specific initiatives in order to increase PPE performance and compliance. In conclusion, there is still work to do in order to fill in the existing knowledge gap with regard to sex, gender, and work, but there are promising initiatives and the field is progressing.

GLOBAL RETROSPECTIVE REVIEW OF SEVERE ACUTE RESPIRATORY SYNDROME SARS COV-2 INFECTIONS IN NONDOMESTIC FELIDS: MARCH 2020-FEBRUARY 2021.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in nondomestic felids have been documented in North America, South America, Africa, Europe, and Asia. Between March 2020 and February 2021, at nine institutions across three continents, infection was confirmed in 16 tigers (Panthera tigris), 14 lions (Panthera leo), three snow leopards (Panthera uncia), one cougar (Puma concolor), and one Amur leopard cat (Prionailurus bengalensis euptilurus) ranging from 2 to 21 yr old (average, 10 yr). Infection was suspected in an additional 12 tigers, 4 lions, and 9 cougars. Clinical signs (in order of most to least common) included coughing, ocular and/or nasal discharge, wheezing, sneezing, decreased appetite, lethargy, diarrhea, and vomiting. Most felids recovered uneventfully, but one geriatric tiger with comorbidities developed severe dyspnea and neurologic signs necessitating euthanasia. Clinical signs lasted 1-19 d (average, 8 d); one tiger was asymptomatic. Infection was confirmed by various methods, including antigen tests and/or polymerase chain reaction (PCR) of nasal or oral swabs, tracheal wash, and feces, or virus isolation from feces or tracheal wash. Infection status and resolution were determined by testing nasal swabs from awake animals, fecal PCR, and observation of clinical signs. Shedding of fecal viral RNA was significantly longer than duration of clinical signs. Postinfection seropositivity was confirmed by four institutions including 11 felids (5 lions, 6 tigers). In most instances, asymptomatic or presymptomatic keepers were the presumed or confirmed source of infection, although in some instances the infection source remains uncertain. Almost all infections occurred despite using cloth facemasks and disposable gloves when in proximity to the felids and during food preparation. Although transmission may have occurred during momentary lapses in personal protective equipment compliance, it seems probable that cloth masks are insufficient at preventing transmission of SARS-CoV-2 from humans to nondomestic felids. Surgical or higher grade masks may be warranted when working with nondomestic felids.

Personal Protective Equipment Detection: A Deep-Learning-Based Sustainable Approach

Personal protective equipment (PPE) can increase the safety of the worker for sure by reducing the probability and severity of injury or fatal incidents at construction, chemical, and hazardous sites. PPE is widely required to offer a satisfiable safety level not only for protection against the accidents at the aforementioned sites but also for chemical hazards. However, for several reasons or negligence, workers may not commit to and comply with the regulations of wearing the equipment, occasionally. Since manual monitoring is laborious and erroneous, the situation demands the development of intelligent monitoring systems to offer the automated real-time and accurate detection of PPE compliance. As a solution, in this study, Deep Learning and Computer Vision are investigated to offer near real-time and accurate PPE detection. The four colored hardhats, vest, safety glass (CHVG) dataset was utilized to train and evaluate the performance of the proposed model. It is noteworthy that the solution can detect eight variate classes of the PPE, namely red, blue, white, yellow helmets, head, person, vest, and glass. A two-stage detector based on the Fast-Region-based Convolutional Neural Network (RCNN) was trained on 1699 annotated images. The proposed model accomplished an acceptable mean average precision (mAP) of 96% in contrast to the state-of-the-art studies in literature. The proposed study is a potential contribution towards the avoidance and prevention of fatal/non-fatal industrial incidents by means of PPE detection in real-time.

Awareness Level and Challenges of Personal Protective Equipment Compliance by Malaysian Construction Workers

Awareness Level and Challenges of Personal Protective Equipment Compliance by Malaysian Construction Workers

The Correlation between Knowledge and Compliance with Personal Protective Equipment (PPE) Use in Construction Workers at EMC Alam Sutera Hospital, South Tangerang City

Construction work has a high-risk workplace. Construction workers are often faced with difficult and dangerous working situations. One of the efforts to protect workers in Occupational Safety and Health (OSH) is by supplying Personal Protective Equipment (PPE). This study analyzes the correlation between knowledge and compliance with personal protective equipment (PPE) used in construction workers. This paper correlated quantitative research with a cross-sectional approach. The study population was all construction workers at EMC Alam Sutera Hospital, South Tangerang, totaling 70 people. There were 70 respondents with a total sampling technique. The instruments were PPE compliance observation sheets and PPE knowledge questionnaires. Data analysis was univariate and bivariate analysis. Results showed that most respondents were 40 or older (60%). In addition, half of them had secondary and tertiary education (54.3%). Most also had good knowledge about PPE (65.7%). Of the 46 construction workers with good PPE knowledge, 42 complied with PPE use (91.3), and only four were not compliant (8.7%). Meanwhile, of the 24 construction workers with poor knowledge of PPE, 11 were not compliant with PPE use (45.8%), and 13 were compliant (54.3%). The statistical test results obtained were p=0.001. Thus, there was a significant correlation between knowledge and compliance with PPE use in respondents. In conclusion, knowledge correlates compliance with PPE uses among construction workers in EMC Alam Sutera Hospital, South Tangerang City. The hospital should provide recommendations to construction companies at EMC Alam Sutera Hospital, South Tangerang City, regarding using full PPE for construction workers.

Analysis of Hand Hygiene Compliance and Official Personal Protective Equipment Compliance on the Incidence of Nosocomial Infection In Outpatient Karanganyar Regencyhospital to Support of Hospital Accreditation

Background: A nosocomial infection is an infection acquired or acquired in a hospital related to the provision of health services in health care facilities, where caregiver behavior includes hand washing, the use of protective equipment, the handling of medical equipment, the disinfection of the action site, the prevention of care and treatment, wound closure, and waste management.
 Objective: To assess officers' compliance with hand hygiene and use of personal protective equipment on the incidence of nosocomial infections in outpatients at Karanganyar District Hospital in support of hospital accreditation.
 Methodology: This study uses a type of quantitative research. The sample technique used total sampling with a population of all outpatient employees at Karanganyar District Hospital.
 Result: Compliance with hand hygiene and adherence to the use of personal protective equipment by officers affected the incidence of nosocomial infections where the F count exceeded the F table (47.313 > 3.10). Meanwhile, partially, handwashing compliance and compliance with the use of personal protective equipment for officers had an effect on the incidence of nosocomial infections.
 Conclusion: Compliance with hand hygiene and adherence to the use of personal protective equipmen

SARS-CoV-2 in lions, gorillas and zookeepers in the Rotterdam Zoo, the Netherlands, a One Health investigation, November 2021.

In November 2021, seven western lowland gorillas and four Asiatic lions were diagnosed with COVID-19 at Rotterdam Zoo. An outbreak investigation was undertaken to determine the source and extent of the outbreak and to identify possible transmission routes. Interviews were conducted with staff to identify human and animal contacts and cases, compliance with personal protective equipment (PPE) and potential transmission routes. Human and animal contacts and other animal species suspected to be susceptible to SARS-CoV-2 were tested for SARS-CoV-2 RNA. Positive samples were subjected to sequencing. All the gorillas and lions that could be tested (3/7 and 2/4, respectively) were RT-PCR positive between 12 November and 10 December 2021. No other animal species were SARS-CoV-2 RNA positive. Forty direct and indirect human contacts were identified. Two direct contacts tested RT-PCR positive 10 days after the first COVID-19 symptoms in animals. The zookeepers' viral genome sequences clustered with those of gorillas and lions. Personal protective equipment compliance was suboptimal at instances. Findings confirm transmission of SARS-CoV-2 among animals and between humans and animals but source and directionality could not be established. Zookeepers were the most likely source and should have periodic PPE training. Sick animals should promptly be tested and isolated/quarantined.

A longitudinal environmental surveillance study for SARS-CoV-2 from the emergency department of a teaching hospital in Hong Kong

Understanding factors associated with SARS-CoV-2 exposure risk in the hospital setting may help improve infection control measures for prevention. To monitor SARS-CoV-2 exposure risk among healthcare workers and to identify risk factors associated with SARS-CoV-2 detection. Surface and air samples were collected longitudinally over 14 months spanning 2020-2022 at the Emergency Department (ED) of a teaching hospital in Hong Kong. SARS-CoV-2 viral RNA was detected by real-time reverse-transcription polymerase chain reaction. Ecological factors associated with SARS-CoV-2 detection were analysed by logistic regression. A sero-epidemiological study was conducted in January to April 2021 to monitor SARS-CoV-2 seroprevalence. A questionnaire was used to collect information on job nature and use of personal protective equipment (PPE) of the participants. SARS-CoV-2 RNA was detected at low frequencies from surfaces (0.7%, n=2562) and air samples (1.6%, n=128). Crowding was identified as the main risk factor, as weekly ED attendance (OR=1.002, p=0.04) and sampling after peak-hours of ED attendance (OR=5.216, p=0.03) were associated with the detection of SARS-CoV-2 viral RNA from surfaces. The low exposure risk was corroborated by the zero seropositive rate among 281 participants by April 2021. Crowding may introduce SARS-CoV-2 into ED through increased attendances. Multiple factors may have contributed to the low contamination of SARS-CoV-2 at the ED, including hospital infection control measures for screening ED attendees, high PPE compliance among healthcare workers, and various public health and social measures implemented to reduce community transmission in Hong Kong where a dynamic zero COVID-19 policy was adopted.

Assessing personal protective equipment compliance in a polish healthcare setting during the COVID-19 pandemic – A pilot case study

Objective: The purpose of this study is to identify failures in proper Personal Protective Equipment (PPE) usage in a healthcare hospital environment to enhance PPE compliance through proper donning and doffing procedures.Methods: We used naturalistic observation (shadowing) of PPE donning and doffing by healthcare medical staff in their hospital work setting to identify non-conformities to compliant donning and doffing of PPE.Results: We found an average of 1.84 non-conformances per healthcare worker across the donning procedures and 2.06 non-conformances in the doffing procedures per healthcare provider. Nurses experienced 1.94 average non-conformances in the donning procedures, while physicians average 1.75 non-conformances. Nurses experienced 2.29 average doffing nonconformances, while physicians averaged 1.85 average doffing non-conformances during the study. PPE compliance is critical to protect both healthcare workers and patients in the healthcare setting, as well as building a culture of safety.Research implications: Appropriate training and compliance should be performed to ensure appropriate PPE donning and doffing protocols are adhered to, so that it reduces the transmission of disease and infections. Future studies will explore the environmental, cultural and operational factors that contribute to PPE compliance in healthcare.Conclusions: This is the first study to quantify donning and doffing errors of personal protective compliance within the realm of environmental and cultural impacts.

Innovate and Stockpile: Respiratory Protection for Essential Workers in a Catastrophic Pandemic.

Innovate and Stockpile: Respiratory Protection for Essential Workers in a Catastrophic Pandemic.

Plaster of Paris (POP) Installers’ Awareness of Occupational Hazards and Utilisation of Safety Measures in Ho Municipality, Ghana

Construction workers are found to use Personal Protective Equipment (PPE) infrequently, although workplace accidents are common and strongly associated with safety education. In Ho, Ghana, this study examines POP installers’ awareness of occupational hazards and the use of PPEs. The study’s motivation and contribution are in its analysis of the perceived obstacles and driving forces behind PPE use by POP installers in the construction industry, which is yet unknown in Ghana. Between May 15 and June 30, 2022, 149 POP installers in Ho Municipality filled out a crosssectional questionnaire using a purposive sampling method, and the data were analysed using the Statistical Package for Social Science (SPSS) v.20. The outcome variables were determined using logistic regression with a 0.05 p value, the relative importance index (RII), and correlation analysis. There is a lack of awareness of knowledge, PPE use, and workplace safety among the participants. Once again, respondents cited discomfort as a reason for not wearing PPE during POP installation. Furthermore, knowledge of occupational hazards among POP workers negatively predicts PPE usage (B = 0.207, p < 0.05). The study’s findings provide critical information for contractors’ associations to collaborate with relevant health and safety professionals to regularly organise safety awareness training for members to increase worker knowledge and reduce risk-taking behaviour. This study is unique because it is the first to access and provides an in-depth analysis of the purported barriers and driving motivations for PPE compliance and non-compliance from the perspective of an under-researched group of construction workers in Ghana.

Factors Influencing Compliance With Personal Protective Equipment (PPE) Use Among Healthcare Workers.

Introduction Accurate and appropriate use of personal protective equipment (PPE) is an integral component in infection prevention and control policy to ensure healthcare workers' safety.Poor compliance with personal protective behaviours and inconsistent use ofPPE has been identified as the main cause of transmission of nosocomial infections in healthcare settings and this reduced compliance is linked to many individual, environmental, and organizational factors. Therefore, the current study was carried out to identify various factors influencing PPE use among healthcare workers. Materials and methods A descriptive cross-sectional survey has been carried out among healthcare workers selected from two selected tertiary care hospitals in central India. Data on compliance with PPE and factors influencing compliance were collected using a three-point rating scale and structured questionnaire. Quantile regression was performed to identify the factors associated with adherence to PPE use among healthcare workers. Results The median score for compliance with PPE use among healthcare workers was found to be 22 with an interquartile range (IQR) of 16-24.The multiple quantile regression found that variables such as occupation (p<0.001), institutional policy (p=0.003), quality of PPE (p=0.002), availability of PPE (p<0.001), and improper size (p=0.042) were significantly associated with PPE compliance by healthcare workers. Conclusion The current study highlights the importance of taking adequate measures by the government and healthcare organizations to eliminate various factors hindering PPE compliance levels among healthcare workers to ensure consistent use of PPE by healthcare workers to safeguard themselves and patients.

Personal Protective Equipment Compliance in Midwifery Services in Eastern Indonesia

At the begining of pademic, Indonesiam goverment suggested that midwifery services be carried by using appropriate personal protective equipment according to the guidelines to avoid cross-infection between health worker to mother and child. The purposes of this strudy is to determine the midwife's compliance in following the use of PPE when administering mother and child health services during COVID 19 pandemic. This survey was conducted at the begining of pandemi COVID-19, 403 registered midwives participated by filled an online survey with Google form application for data collection. Variable of this research are midwife compliance of using PPE in midwifery services. The data were analyzed using the statistical test used was Pearson's chi-square. Results showed that 89.8% of midwives were not following the health protocol, as 49.8% offered inadequate PPE in the workplace and the price of PPE was very high during the pandemic. Conclusions: Midwives were unable to comply with the use of personal health and safety equipment (PPE) in services. Age and duration of work had factor had a significant impact on whether or not midwives were able to use PPE appropriately.

Enhanced safety implementation in 5S + 1 via object detection algorithms

Scholarly work points to 5S + 1, a simple yet powerful method of initiating quality in manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production Systems. The 6th S, safety, is often used to prevent future occupational hazards, therefore, reducing the loss of time, money, and human resources. This paper aims to show how Industry 4.0 technologies such as computer-based vision and object detection algorithms can help implement the 6th S in 5S + 1 through monitoring and detecting workers who fail to adhere to standard safety practices such as wearing personal protective equipment (PPE). The paper evaluated and analyzed three different detection approaches and compared their performance metrics. In total, seven models were proposed to perform such a task. All the proposed models utilized You-Only-Look-Once (YOLO v7) architecture to verify workers’ PPE compliance. In approach I, three models were used to detect workers, safety helmets and safety vests. Then, a machine learning algorithm was used to verify if each detected worker is in PPE compliance. In approach II, the model simultaneously detects individual workers and verifies PPE compliance. In approach III, three different models were used to detect workers in the input feed. Then, a deep learning algorithm was used to verify the safety. All models were trained on Pictor-v3 dataset. It is found that the third approach, when utilizing VGG-16 algorithm, achieves the best performance, i.e., 80% F1 score, and can process 11.79 frames per second (FPS), making it suitable for real-time detection.

Real-Time Personal Protective Equipment Compliance Detection Based on Deep Learning Algorithm

The construction industry is one of the most dangerous industries in the world due to workers being vulnerable to accidents, injuries and even death. Therefore, how to effectively manage the appropriate usage of personal protective equipment (PPE) is an important research issue. In this study, deep learning is applied to the PPE inspection model to verify whether construction workers are equipped in accordance with the regulations, and this is expected to reduce the probability of related occupational disasters caused by the inappropriate use of PPE. The method is based on the YOLOv3, YOLOv4 and YOLOv7 algorithms to detect worker’s helmets and high-visibility vests from images or videos in real time. The model was trained on a new PPE dataset collected and organized by this study; the dataset contains 11,000 images and 88,725 labels. According to the test results, can achieve a 97% mean average precision (mAP) and 25 frames per second (FPS). The research result shows that the detection and counting data in this method have performed well and can be applied to the real-time PPE detection of workers at the construction job site.

Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions.

The term "smart lab" refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances' size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student's PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students.