Personal Protective Equipment Compliance Research Articles

Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities.

The COVID-19 pandemic has created an urgent demand for research, which has spurred the development of enhanced biosafety protocols in biosafety level (BSL)-3 laboratories to safeguard against the risks associated with handling highly contagious pathogens. Laboratory management failures can pose significant hazards. An external system captured images of personnel entering a laboratory, which were then analyzed by an AI-based system to verify their compliance with personal protective equipment (PPE) regulations, thereby introducing an additional layer of protection. A deep learning model was trained to detect the presence of essential PPE items, such as clothing, masks, hoods, double-layer gloves, shoe covers, and respirators, ensuring adherence to World Health Organization (WHO) standards. The internal laboratory management system used a deep learning model to delineate alert zones and monitor compliance with the imposed safety protocols. The external detection system was trained on a dataset consisting of 4112 images divided into 15 PPE compliance classes. The model achieved an accuracy of 97.52% and a recall of 97.03%. The identification results were presented in real time via a visual interface and simultaneously stored on the administrator's dashboard for future reference. We trained the internal management system on 3347 images, achieving 90% accuracy and 85% recall. The results were transmitted in JSON format to the internal monitoring system, which triggered alerts in response to violations of safe practices or alert zones. Real-time notifications were sent to the administrators when the safety thresholds were met. The BSL-3 laboratory monitoring system significantly reduces the risk of exposure to pathogens for personnel during laboratory operations. By ensuring the correct use of PPE and enhancing adherence to the imposed safety protocols, this system contributes to maintaining the integrity of BSL-3 facilities and mitigates the risk of personnel becoming infection vectors.

ASSESSMENT OF HEALTHCARE WORKERS' KNOWLEDGE AND ATTITUDES REGARDING PPE USAGE, INCLUDING BARRIERS AND FACILITATORS TO COMPLIANCE WITH RECOMMENDED GUIDELINES IN NISHTAR HOSPITAL MULTAN

Personal protective equipment (PPE) is a critical component of infection prevention and control, particularly in resource-limited healthcare settings like Pakistan. Compliance with PPE guidelines depends on healthcare workers' (HCWs) knowledge, attitudes, and the systemic barriers and facilitators they encounter. Objective: To assess HCWs' knowledge and attitudes regarding PPE usage, identify barriers and facilitators to compliance, and propose targeted interventions for improving adherence to PPE guidelines at Nishtar Hospital Multan. Methods: This descriptive cross-sectional study included 250 HCWs selected through simple random sampling. Data were collected using a structured, pre-tested questionnaire covering demographic details, knowledge and attitudes about PPE, and perceived barriers and facilitators to compliance. Statistical analysis was performed using IBM SPSS version 26, with results presented as frequencies and percentages. Chi-square tests were used to evaluate associations, with a p-value ≤0.05 considered significant. Results: Most participants (75%) correctly identified all PPE components, while 68% demonstrated adequate knowledge of proper donning procedures. Approximately 85% of HCWs recognized PPE as essential for infection control, but 60% found its usage inconvenient. The primary barriers to compliance were lack of training (60%) and limited availability (40%), while key facilitators included regular training programs (72%), accessible PPE supplies (80%), and strong leadership support (64%).Conclusion: Significant knowledge gaps and systemic barriers to PPE compliance were identified among HCWs at Nishtar Hospital. Improving training programs, ensuring consistent PPE availability, and fostering leadership engagement are essential steps toward enhancing compliance and strengthening infection control practices in resource-constrained settings.

Compliance with personal protective equipment use among clay works employees

This study examined how personal factors: knowledge and attitudes; and company factors: commitment, availability of personal protective equipment (PPE), adequacy of equipment, and work arrangements are compliant with the use of PPE among Clay Works factory employees. Failure of compliance results in occupation health problems. Hence, a need to examine both personal and institutional factors in this factory. The study employed descriptive and analytical cross-sectional research designs with qualitative and quantitative data collection approaches. Data was collected from a purposively selected 133 employees and 5 management staff of Unique Clay Works Limited. Quantitative data was collected using a questionnaire, which was distributed to all 133 respondents. Qualitative data were collected using face-to-face interviews. Quantitative data was analysed using descriptive statistics and qualitative data was analysed using coding. The results show that the level of compliance with the use of PPE was low (30.1%). The regression-adjusted Odd Ratio shows that females are 4.95% more likely to comply than males. At the same time, commitment by management accounted for 17.56% (p < 0.05). It is recommended that employers need to consider gender as an important factor regarding PPE compliance in the company and that companies uphold PPE compliance and also assess productivity as a consequence of compliance.

Deep learning for the detection of good hygienic practices control measures for food handlers

Hygiene habits and behaviors of food handlers are critical to ensuring food safety during production. Good Hygiene Practices (GHP) require food handlers to follow strict hygiene standards, including proper handwashing procedures and the use of personal protective equipment (PPE) such as hairnets, masks, gloves, protective clothing, pants, and work shoes to prevent cross-contamination in food factories. However, manual inspections in large-scale operations are often inefficient and impractical. To address this, this study proposes a deep learning-based system that leverages computer vision and Convolutional Neural Networks (CNNs) to automatically detect hand hygiene and PPE compliance before workers enter the production area. This ensures adherence to GHP standards, enhances food safety, improves inspection efficiency, and reduces costs. A dataset of 20,222 entries, comprising 12 handwashing actions from 8 angles and color-coded PPE conditions, was used to train and evaluate 10 models: YOLOv8, YOLOv7, YOLOv6, YOLOv5, ResNet, Dense-Net, MobileNetv2, EfficientNetv2, VGG, and Vision Transformer (ViT). YOLOv6 and YOLOv8 achieved the highest accuracy (0.999) for handwashing recognition, while Dense-Net achieved the highest accuracy (0.956) for PPE detection. This system offers an efficient and automated solution for monitoring hygiene habits, helping to prevent cross-contamination and ensure food safety within factory environments.

A systematic review of computer vision-based personal protective equipment compliance in industry practice: advancements, challenges and future directions

Computerized compliance of Personal Protective Equipment (PPE) is an emerging topic in academic literature that aims to enhance workplace safety through the automation of compliance and prevention of PPE misuse (which currently relies on manual employee supervision and reporting). Although trends in the scientific literature indicate a high potential for solving the compliance problem by employing computer vision (CV) techniques, the practice has revealed a series of barriers that limit their wider applications. This article aims to contribute to the advancement of CV-based PPE compliance by providing a comparative review of high-level approaches, algorithms, datasets, and technologies used in the literature. The systematic review highlights industry-specific challenges, environmental variations, and computational costs related to the real-time management of PPE compliance. The issues of employee identification and identity management are also discussed, along with ethical and cybersecurity concerns. Through the concept of CV-based PPE Compliance 4.0, which encapsulates PPE, human, and company spatio-temporal variabilities, this study provides guidelines for future research directions for addressing the identified barriers. The further advancements and adoption of CV-based solutions for PPE compliance will require simultaneously addressing human identification, pose estimation, object recognition and tracking, necessitating the development of corresponding public datasets.

A Study on the Use of Personal Protective Equipment among Construction Workers in Türkiye

This study examines the habits and attitudes of construction sector employees in Türkiye regarding the use of personal protective equipment (PPE). A comprehensive face-to-face survey was conducted with 398 randomly selected employees from small- and medium-sized construction companies across various regions of Türkiye. The survey aimed to identify the frequency of PPE usage and the factors influencing its use and to provide actionable suggestions for improving PPE compliance. The results indicate that while a majority of workers recognize the importance of PPE, actual usage rates are inconsistent. Key factors affecting PPE use include the ergonomics and comfort of the equipment as well as worker perceptions and attitudes. Notably, the study found that the discomfort associated with PPE and the belief that it slows down work pace are significant barriers to its regular use. Additionally, the research highlights the need for a robust punishment–reward system to enforce PPE compliance and suggests that more stringent legislative measures are required to enhance occupational health and safety (OHS) standards in the construction industry. This study contributes to the existing body of knowledge by providing a detailed analysis of PPE usage in the Turkish construction sector and offering practical recommendations for policymakers and industry stakeholders. Implementing these suggestions can lead to improved worker safety and reduced occupational accidents.

Personal Protective Equipment Compliance In Preventing Hearing Loss In Agro-Industrial Area

Background: In Indonesia the impact of noise that causes hearing loss is still high compared to other countries in Southeast Asia. Noise can cause hearing loss in workers if the scale of the noise is high and occurs continuously. It can also trigger a decline in health that will have an impact on the performance of workers. Objective: to Analysis of PPE Compliance in Preventing Hearing Loss in Agro-Industrial Area. Methods: The research in this study used the literature review method. This search strategy uses several databases such as Pubmed, ResearchGate, and Google Scholar. This search use keywords PPE, Hearing Loss, and Agro-industrial. Results: Increasing noise intensity, duration of work, and duration of exposure can cause hearing loss because the inner hair cells and supporting cells are damaged. The longer the working period, the greater the noise exposure it will receive. The use of PPE can effectively prevent noise-induced hearing loss. Conclusion: Workers exposed to noise are highly susceptible to hearing loss. It depends on the intensity of the noise, the duration of exposure, the sensitivity of the individual or the worker itself and the frequency of the noise. Noise can be prevented, one of which is by using personal protective equipment when working. In this case, nurses can educate related to the use of PPE to prevent hearing loss.

Occupational Safety: PPE Use and Hazard Experiences among Welders in Valencia City, Bukidnon

This study aimed to determine the significant difference on occupational hazards and the use of personal protective equipment when respondents are grouped according to their length of service and type of training undertaken among welders in Valencia City, Bukidnon. The study employed a quantitative approach utilizing a descriptive-comparative research design. Using non-probability sampling, 46 formally trained welders and another 51 informally trained welders in Valencia City, Bukidnon, were chosen as the respondents for this study. In the demographic profile, the findings showed that the majority, or 54%, of the respondents had 0 to 5 years of experience in terms of their length of service. Based on the statistical data, it was revealed that the level of experience in occupational hazards for both formally trained and informally trained welders was low. Additionally, the level of use of personal protective equipment (PPE) for formally trained welders was high, while for informally trained welders, it was moderate. When the test of difference was executed, the finding showed that the experience of occupational hazards does not differ when grouped according to the types of training undertaken. This denotes that formally and informally trained welders equally experience welding-related occupational hazards. Moreover, another finding showed that the use of personal protective equipment (PPE) differed according to the type of training undertaken. This finding suggests that formally trained welders are more likely to adhere to prescribed safety protocols and utilize recommended PPE, indicating a stronger awareness of occupational hazards and a deeper understanding of the importance of protective measures. Furthermore, the finding revealed that welders’ experience of occupational hazards differs by length of service. This denotes that veteran welders encounter a higher level of occupational hazards compared to their less-experienced counterparts. Subsequently, the last finding revealed that there is no significant difference in the use of personal protective equipment (PPE) among the welders when grouped according to length of service. The researchers recommend that the emphasis on PPE compliance might be ingrained uniformly across all levels of experience, reflecting a robust culture of safety consciousness and an understanding of the indispensable role of protective gear in mitigating occupational risks.

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.

Multipronged strategy for protection and motivation of healthcare workers during the COVID-19 pandemic: a real-life study.

We aimed to assess risk of COVID-19 infection & seroprotection status in healthcare workers (HCWs) in both hospital and community settings following an intensive vaccination drive in India. Tertiary Care Hospital. We surveyed COVID-19 exposure risk, personal protective equipment (PPE) compliance, vaccination status, mental health & COVID-19 infection rate across different HCW cadres. Elecsys® test for COVID-19 spike (Anti-SARS-CoV-2S; ACOVs) and nucleocapsid (Anti-SARS-CoV-2; ACOV) responses following vaccination and/or COVID-19 infection were measured in a stratified sample of 386 HCW. We enrolled 945 HCWs (60.6% male, age 35.9 ± 9.8 years, 352 nurses, 211 doctors, 248 paramedics & 134 support staff). Hospital PPE compliance was 90.8%. Vaccination coverage was 891/945 (94.3%). ACOVs neutralizing antibody was reactive in 381/386 (98.7%). ACOVs titer (U/ml) was higher in the post-COVID-19 infection group (N =269; 242.1 ± 35.7 U/ml) than in the post-vaccine or never infected subgroup (N = 115, 204.1 ± 81.3 U/ml). RT PCR + COVID-19 infections were documented in 224/945 (23.7%) and 6 HCWs had disease of moderate severity, with no deaths. However, 232/386 (60.1%) of HCWs tested positive for nucleocapsid ACOV antibody, suggesting undocumented or subclinical COVID-19 infection. On multivariate logistic regression, only female gender [aOR 1.79, 95% CI 1.07-3.0, P = .025] and COVID-19 family contact [aOR 5.1, 95% CI 3.84-9.5, P < .001] were predictors of risk of developing COVID-19 infection, independent of association with patient-related exposure. Our HCWs were PPE compliant and vaccine motivated, with immunization coverage of 94.3% and seroprotection rate of 98.7%. There was no relationship between HCW COVID-19 infection to exposure characteristics in the hospital. Vaccination reduced disease severity and prevented death in HCW.

A real-time system for detecting personal protective equipment compliance using deep learning model YOLOv5

The manufacturing and construction sectors report high rates of occupational accidents and diseases. In response, many countries, including Malaysia, have incorporated the use of Personal Protective Equipment (PPE) into their government laws and safety regulations. In Malaysia, this requirement is outlined in Standard Operating Procedure (SOP) manuals in the construction sector, which specify the mandatory use of seven types of PPE: hard hats, safety goggles, high-visibility vests, safety shoes, gloves, respirators, and earplugs. However, monitoring a large number of workers and ensuring consistent use of PPE on construction sites can be a challenging task if done manually by site supervisors. Therefore, this project aims to implement a real-time PPE detection system using a deep learning model to effectively monitor workers’ safety compliance. This system can generate reports that companies in the appropriate sectors can use to monitor compliance with the Occupational Safety and Health Act (OSHA). The prototype system was developed as a web-based platform. The object detection deep learning model achieved a mean Average Precision (mAP) of 91.5% in the metric evaluation, while the accuracy testing yielded a 98.3% accuracy rate. For future work, it is suggested to enhance the detection model for other types of PPE and to make the system more reliable across all sectors by expanding the training dataset.

Real-Time Monitoring of Personal Protective Equipment Compliance in Surveillance Cameras

Real-Time Monitoring of Personal Protective Equipment Compliance in Surveillance Cameras

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.