Safety Of Workers In Industry Research Articles

Lightweight Helmet Detection Algorithm Using an Improved YOLOv4.

Safety helmet wearing plays a major role in protecting the safety of workers in industry and construction, so a real-time helmet wearing detection technology is very necessary. This paper proposes an improved YOLOv4 algorithm to achieve real-time and efficient safety helmet wearing detection. The improved YOLOv4 algorithm adopts a lightweight network PP-LCNet as the backbone network and uses deepwise separable convolution to decrease the model parameters. Besides, the coordinate attention mechanism module is embedded in the three output feature layers of the backbone network to enhance the feature information, and an improved feature fusion structure is designed to fuse the target information. In terms of the loss function, we use a new SIoU loss function that fuses directional information to increase detection precision. The experimental findings demonstrate that the improved YOLOv4 algorithm achieves an accuracy of 92.98%, a model size of 41.88 M, and a detection speed of 43.23 pictures/s. Compared with the original YOLOv4, the accuracy increases by 0.52%, the model size decreases by about 83%, and the detection speed increases by 88%. Compared with other existing methods, it performs better in terms of precision and speed.

Biomechanical risk in fatiguing frequency-dependent lifting activities: muscle coactivation in people with and without low back pain

Musculoskeletal diseases and disorders from biomechanical overload are very common among workers. In Italy in2019, occupational diseases of the osteomuscular system and connective tissue accounted for 66% of the total number ofdiseases reported to INAIL. Many factors can contribute to the establishment of a condition of biomechanical overloadand therefore to the onset of work-related musculoskeletal disorders (WMSDs). Among these, work-related low-backdisorders (WLBDs), caused mainly by handling heavy loads, are very common.In recent years, several methods have been developed to assess the risk of biomechanical overload, included in severalinternational standards (ISO-11228, ISO-11226, ISO/TR 12295 and 12296) aimed at identifying high-risk work activities and assessing the effectiveness of ergonomic interventions. Among the best known, with regard to the manual liftingof heavy loads, there is the Revised NIOSH Lifting Equation that, while presenting many advantages (cost-effectiveness,non-invasiveness, speed of application ...) at the same time also has limitations concerning mainly the high subjectivity(subject of scientific debate) and the impossibility of these methods to assess all work tasks.From these premises, it is clear the usefulness of being able to use new quantitative risk assessment methodologies,objectifiable and repeatable, which provide for the possibility of assessing the risk from biomechanical overload evenin modern working scenarios where the use of exoskeletons by workers and the sharing of working space with cobots isbecoming increasingly widespread. In fact, the methods currently used are incomplete and ineffective in assessing thereal impact that these technologies have on the health and safety of workers in Industry 4.0.Recent studies (some of which we were involved in) have introduced the possibilities offered by optoelectronic systems, inertial sensors (IMUs) and surface electromyography (sEMG), to integrate the most widely used observationalmethodologies. These modern technologies, evaluating how a subject moves his joints and uses his muscles during theexecution of a work task, can integrate the observational methods, quantify the elements that characterize the risk minimizing the evaluation errors caused by individual subjectivity and allow to carry out the assessment of biomechanicalrisk even in those areas where the currently most widespread methodologies are not able to give exhaustive answers. Inparticular, the innovative methodologies based on IMUs and sEMG, allow the instrumental quantitative assessment ofbiomechanical risk directly in the field thanks to the fact that the sensors are miniaturized, wearable, easily transportableand based on “wireless” transmission of data acquired on the worker who performs the task. These aspects facilitatedata recording, allowing accurate signal acquisition even in unfavorable environments and in work situations where theworker interacts with a cobot or uses an exoskeleton. Previous studies have involved studies of non-fatiguing lifts, wherethe movement and relative risk of single repetitions of lifting were studied. Currently, we wonder what happens when thework activity becomes fatiguing and whether it is still possible to use these methods to classify risk. In addition, anotherunexplored question concerns the presence of workers who continue to perform work activity during the first phase ofonset of musculoskeletal disorders: can the risk to which these workers are exposed be considered the same as thatinvolving workers without pain? To answer these questions, we conducted an experimental campaign at the Universityof Birmingham in collaboration with Roma Tre University and INAIL in which subjects with and without back disordersperformed fatiguing lifts of 15 minutes in three risk levels determined by three different lifting frequencies. We studiedtrunk muscle activity in terms of muscle coactivation of the trunk flexor and extensor muscles. The results show howcoactivation can classify risk during manual load lifting activities by distinguishing not only the level of risk but alsothe presence or absence of back disorders. These results suggest that the use of electromyographic features to assess thebiomechanical risk associated with work activities can also be used in the presence of fatiguing lifting also to distinguishthe risk in case of subjects with back pain. This methodology could be used to monitor fatigue and extend the possibilitiesoffered by currently available instrumental-based approaches.

Biomechanical risk in fatiguing frequency-dependent lifting activities: muscle coactivation in people with and without low back pain

Musculoskeletal diseases and disorders from biomechanical overload are very common among workers. In Italy in 2019, occupational diseases of the osteomuscular system and connective tissue accounted for 66% of the total number of diseases reported to INAIL. Many factors can contribute to the establishment of a condition of biomechanical overload and therefore to the onset of work-related musculoskeletal disorders (WMSDs). Among these, work-related low-back disorders (WLBDs), caused mainly by handling heavy loads, are very common. In recent years, several methods have been developed to assess the risk of biomechanical overload, included in several international standards (ISO-11228, ISO-11226, ISO/TR 12295 and 12296) aimed at identifying high-risk work activities and assessing the effectiveness of ergonomic interventions. Among the best known, with regard to the manual lifting of heavy loads, there is the Revised NIOSH Lifting Equation that, while presenting many advantages (cost-effectiveness, non-invasiveness, speed of application ...) at the same time also has limitations concerning mainly the high subjectivity (subject of scientific debate) and the impossibility of these methods to assess all work tasks. From these premises, it is clear the usefulness of being able to use new quantitative risk assessment methodologies, objectifiable and repeatable, which provide for the possibility of assessing the risk from biomechanical overload even in modern working scenarios where the use of exoskeletons by workers and the sharing of working space with cobots is becoming increasingly widespread. In fact, the methods currently used are incomplete and ineffective in assessing the real impact that these technologies have on the health and safety of workers in Industry 4.0. Recent studies (some of which we were involved in) have introduced the possibilities offered by optoelectronic systems, inertial sensors (IMUs) and surface electromyography (sEMG), to integrate the most widely used observational methodologies. These modern technologies, evaluating how a subject moves his joints and uses his muscles during the execution of a work task, can integrate the observational methods, quantify the elements that characterize the risk minimizing the evaluation errors caused by individual subjectivity and allow to carry out the assessment of biomechanical risk even in those areas where the currently most widespread methodologies are not able to give exhaustive answers. In particular, the innovative methodologies based on IMUs and sEMG, allow the instrumental quantitative assessment of biomechanical risk directly in the field thanks to the fact that the sensors are miniaturized, wearable, easily transportable and based on "wireless" transmission of data acquired on the worker who performs the task. These aspects facilitate data recording, allowing accurate signal acquisition even in unfavorable environments and in work situations where the worker interacts with a cobot or uses an exoskeleton. Previous studies have involved studies of non-fatiguing lifts, where the movement and relative risk of single repetitions of lifting were studied. Currently, we wonder what happens when the work activity becomes fatiguing and whether it is still possible to use these methods to classify risk. In addition, another unexplored question concerns the presence of workers who continue to perform work activity during the first phase of onset of musculoskeletal disorders: can the risk to which these workers are exposed be considered the same as that involving workers without pain? To answer these questions, we conducted an experimental campaign at the University of Birmingham in collaboration with Roma Tre University and INAIL in which subjects with and without back disorders performed fatiguing lifts of 15 minutes in three risk levels determined by three different lifting frequencies. We studied trunk muscle activity in terms of muscle coactivation of the trunk flexor and extensor muscles. The results show how coactivation can classify risk during manual load lifting activities by distinguishing not only the level of risk but also the presence or absence of back disorders. These results suggest that the use of electromyographic features to assess the biomechanical risk associated with work activities can also be used in the presence of fatiguing lifting also to distinguish the risk in case of subjects with back pain. This methodology could be used to monitor fatigue and extend the possibilities offered by currently available instrumental-based approaches.

IoT applied to work safety in industry 4.0 at the amazonas industrial pole

The study aims to find out factors The Internet of Things (IoT) approach - is a technology that is reconfiguring the sectors of activity, changing the definitions of how objects are connected to each other, today it is being applied in companies and industries, transforming the scenario how companies compete, changing the business models, structure and process of organizations. The following article aims to conceptualize IoT, work safety, industry 4.0 and analyze how these applications are implemented in companies in the industrial pole of Amazonas. We sought to understand and identify through the concepts the challenges and impacts on companies and industries. With the advancement of new technologies, the Internet of Things has been transforming and changing showing that companies are investing in different smart technologies, that is, IoT has become one of the pillars of industry 4.0, bringing modernity, security and optimization of the manufacturing of industries as quick data analysis, production decision making and cost reduction. At the end of the elaboration of this work, it was observed that the chosen theme is of great importance mainly in the transition of new technologies, security and information. A bibliographic search was made, with references and information able to form a more comprehensive analysis on the topic.

IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers

Pollution is adding contaminants into the nature that causes an adverse change in the environment. Air pollution is one of the highest mortality risk factors globally. The sources of air pollution in the industries are power plants, chamber process, cleaning, burning of materials, etc. A variety of pollutants emitted into the air such as sulfur dioxide, carbon monoxide, carbon dioxide, ammonia and volatile organic compounds. Particulate Matter (PM) is an air pollutant that is the mixture of solid dust or pollen and liquid droplets with air. Air pollution in industrial workplaces is a major concern and monitoring and management of it to be addressed to protect the industrial workers health from the air pollution effects. The people are suffering from several respiratory and heart issues along with cancer due to increasing air pollution. This device is composed of ESP32 MCU, MQ135 gas sensor, SDS011 optical dust particle sensor, and BME280 humidity and temperature sensor for monitoring the air quality. The gas sensor MQ135 senses the harmful gases present in the environment. SDS011 optical dust sensor senses the PM levels (PM10 and PM2.5) in the atmosphere. The sensor values are evaluated for the Air Quality Index (AQI) and display it on the ThingSpeak IoT platform. Vrituino app has used for a virtual screen with widgets on the mobile phone to monitor the system using the web. In order to improve the real-time performance of the system, an IoT and a cloud computing technology are being used. The ESP32 turns on the fan units to maintain the pollutants within the safe limit when the presence of harmful gases and PM levels exceeds a certain threshold level. This system is essential for industrial work places to adopt measures and control air pollution which increase industrial workers safety.

IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers

Pollution is adding contaminants into the nature that causes an adverse change in the environment. Air pollution is one of the highest mortality risk factors globally. The sources of air pollution in the industries are power plants, chamber process, cleaning, burning of materials, etc. A variety of pollutants emitted into the air such as sulfur dioxide, carbon monoxide, carbon dioxide, ammonia and volatile organic compounds. Particulate Matter (PM) is an air pollutant that is the mixture of solid dust or pollen and liquid droplets with air. Air pollution in industrial workplaces is a major concern and monitoring and management of it to be addressed to protect the industrial workers health from the air pollution effects. The people are suffering from several respiratory and heart issues along with cancer due to increasing air pollution. This device is composed of ESP32 MCU, MQ135 gas sensor, SDS011 optical dust particle sensor, and BME280 humidity and temperature sensor for monitoring the air quality. The gas sensor MQ135 senses the harmful gases present in the environment. SDS011 optical dust sensor senses the PM levels (PM10 and PM2.5) in the atmosphere. The sensor values are evaluated for the Air Quality Index (AQI) and display it on the ThingSpeak IoT platform. Vrituino app has used for a virtual screen with widgets on the mobile phone to monitor the system using the web. In order to improve the real-time performance of the system, an IoT and a cloud computing technology are being used. The ESP32 turns on the fan units to maintain the pollutants within the safe limit when the presence of harmful gases and PM levels exceeds a certain threshold level. This system is essential for industrial work places to adopt measures and control air pollution which increase industrial workers safety.

Safety index: a systematic approach to measure the level of occupational safety in manufacturing industry

Despite the advancement in technology, workplace safety in the industry still needs improvements. This study aimed to develop a new safety index, termed as occupational safety evaluation approach (OSEA) index, by using Delphi method. This approach considered 40 factors that were validated with content validity index (CVI). The ideal value of OSEA obtained is 21.07, which is further used for evaluating and mapping the current level of occupational safety of workers in medium-sized iron and steel manufacturing industry. In order to estimate OSEA, a questionnaire-based survey was conducted in 30 medium sized manufacturing industries (MSMI) of Punjab. The results revealed that the OSEA index of MSMI lies in the range of 8.90-17.79. Moreover, it is concluded that OSEA index is a useful tool to measure the existing level of occupational safety of workers in the industry, which can be implemented to assess the level of workplace safety.

VOC Free Epoxy Resin/Dicyandiamide 경화물의 배합비 변화에 따른 물리적 특성 및 열적특성 분석

Volatile organic compounds (VOC) free adhesives have been interested by many scientists and engineers due to envi- ronmental regulations and the safety of industrial workers. In this work, a series of composites composed with bisphenol A epoxy resin used as solvent, dicyandiamide, and promoter were prepared to investigate the most appropriate molar ratio for steel-steel adhesion. The cured test specimen of each composite were measured with universal testing machine (UTM) to figure out mechanical properties such as tensile strength, Young's modulus, and elongation. Furthermore, the lap shear strength of the specimen was tested with UTM while impact resistance was measured with Izod impact tester. The composite whose molar ratio of epoxy resin to curing agent is 1 : 0.9 (sample 3), showed better tensile strength, coefficient of elastic modulus, elongation, and impact strength than other com- posites did. The highest tanδ from dynamic mechanical analysis (DMA) was observed from sample 2 (epoxy resin: dicy = 1 : 0.7) while sample 3 showed slightly lower tanδ than that of 2. The morphology of the fracture surface of the cured composites from SEM showed that the number of subtle lines on the surface caused by impact increase as the contents of amine curing agent accrete. Furthermore, the viscosity change of sample 5 (epoxy resin: dicy = 1 : 1.3) was observed to confirm its storage stability.

Life, Health, and Safety of Industrial Workers in Bangladesh

Life, Health, and Safety of Industrial Workers in Bangladesh

Undisclosed chemicals — implications for risk assessment: A case study from the mining industry

Many of the chemicals used in industry can be hazardous to human health and the environment, and some formulations can have undisclosed ingredients and hazards, increasing the uncertainty of the risks posed by their use. The need for a better understanding of the extent of undisclosed information in chemicals arose from collecting data on the hazards and exposures of chemicals used in typical mining operations (copper, platinum and coal). Four main categories of undisclosed chemicals were defined (incomplete disclosure; chemicals with unspecific identities; relative quantities of ingredients not stated; and trade secret ingredients) by reviewing material safety data sheet (MSDS) omissions in previous studies. A significant number of chemicals (20% of 957 different chemicals) across the three sites had a range of undisclosed information, with majority of the chemicals (39%) having unspecific identities. The majority of undisclosed information was found in commercially available motor oils followed by cleaning products and mechanical maintenance products, as opposed to reagents critical to the main mining processes. All three types of chemicals had trade secrets, unspecific chemical identities and incomplete disclosures. These types of undisclosed information pose a hindrance to a full understanding of the hazards, which is made worse when combined with additional MSDS omissions such as acute toxicity endpoints (LD50) and/or acute aquatic toxicity endpoints (LC50), as well as inadequate hazard classifications of ingredients. The communication of the hazard information in the MSDSs varied according to the chemical type, the manufacturer and the regulations governing the MSDSs. Undisclosed information can undermine occupational health protection, compromise the safety of workers in industry, hinder risk assessment procedures and cause uncertainty about future health. It comes down to the duty of care that industries have towards their employees. With a wide range of chemicals increasingly used, there is a balance that needs to be reached between disclosure requirements, trade secret provisions and definitions of hazardous ingredients for market needs, and the information required to protect the health of their workers.

Year of worker safety in industry

Year of worker safety in industry

The safety report legislation and its application in the Netherlands

In The Netherlands the health and safety of workers in industry is safeguarded by means of the Safety Act. Recently a paragraph has been added to this

Health and Safety of Industrial Workers

THE centenary of the Factory Act in 1935 led to a good deal of attention being directed to the inadequacy of the Factory and Workshops Act, 1901, which is largely obsolete, and the need for a new Act for the regulation of industry in Great Britain has been widely admitted during the last fifteen years both inside and outside Parliament. More than once, indeed, bills have been introduced; only to be laid on one side in favour of other matters said to be more pressing.