An Evaluation of Work Posture and Musculoskeletal Disorder Risk Level Identification for the Fireworks Industry Worker's

Indumathi, N.; Ramalakshmi, R.

doi:10.1109/icrito51393.2021.9596532

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Multiple methods of evaluation and metrics are employed in this study to assess the proposed method's effectiveness in predicting the HFs influencing the fireworks accident training set. Accuracy, specificity, sensitivity, precision, and F1-score are metrics used in Equation (7).…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Nallathambi

Savaram²,

Sengan

et al. 2023

Sensors

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In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study’s objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

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Section: Evaluation Metricsmentioning

confidence: 99%

“…Sivakasi is known for its firecrackers, matchbox, and printing industries. These industries employ over 25,000 people and generate approximately USD 20 billion [7]. Hazardous chemicals are used in different manufacturing processes at this company.…”

Section: Introductionmentioning

confidence: 99%

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Nallathambi

Savaram²,

Sengan

et al. 2023

Sensors

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show abstract

“…Pergerakan dalam penggunaan meja belajar tergolong cukup sedikit atau minim pergerakan, hal itu dapat menyebabkan pengguna meja belajar terdiam dalam posisi tubuh yang sama dalam beberapa saat. Ketika melakukan kegiatan seseorang dipengaruhi oleh cedera otot, saraf, sumsum tulang belakang, dan lain sebagainya tergantung pada sifat pekerjaan dan tekanan kerja [1]. Kenyamanan dalam melakukan pekerjaan dapat diimplementasikan dengan menerapkan ilmu ergonomi untuk perancangan pada sistem kerja [2].…”

Section: Pendahuluanunclassified

“…Pada saat bekerja postur dilakukan dirancang agar terjadi alamiah sehingga dapat mengurangi timbulnya cedera muskolskeletas. Posisi postur dalam kondisi tidak berubah dalam jangka waktu yang berlebihan mengakibatkan postur tubuh yang buruk [1].…”

Section: Pendahuluanunclassified

Analisis Indikasi Resiko Postur Pengguna Desain Meja Belajar Portabel

Rohmatin,

Wahyuni,

Raharja

2023

J. Konversi Energi dan Manufaktur

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Study portable desks can help people easily move from one another place. Using a study portable desk would make users fix the position once a few moments so that they risk experiencing bodily discomfort. Analysis indications of posture risk can be done by selecting launch rapid upper limb assessment (RULA) analysis from the ergonomic tools toolbar in CATIA software. This research aims to identify movements that occur when using a portable study desk and to analyze user posture risks that can be experienced when using a portable study desk. Based on the research results, it is known that the body posture of portable study desk users for group A on the upper arms is in the range of 20-45°, and the lower arms are in the range of 60-100°. The wrist enters the neutral position range. The rotation of the wrist can be seen that the hand is pointing inward. It has a degree of freedom of 90° with a maximum flexion angle of 160° and a minimum extension angle of 0°. Group B body posture for the stem body has a degree of freedom of 0°. The legs experience a slight downward movement with a degree of freedom of 6.061° and a maximum flexion angle of 135°. Posture analysis results for body parts. Details of the upper arm, forearm, wrist, and wrist twist have a score of 1, which means no posture needs to be corrected and the posture is acceptable. Details of body parts, namely neck, trunk, and legs, are indicated in green, which means no posture needs to be corrected and the posture is acceptable. The score results for body parts with the static posture option get an assessment score of 2 with a green color indication, so it can be concluded that the risk indication for this posture is acceptable.

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“…Professional drivers are more vulnerable to the effects of weariness, which contributes to a greater rate of commercial vehicle collisions [ 10 ]. Bus drivers have sensitive and demanding jobs that are complicated by a variety of risk factors, including potential traffic jams, a lack of knowledge about transportation schedules, irregular work schedules, complicated routes, inclement weather, and an increase in the density, intensity, and speed of the flow of traffic.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning

Hanumegowda

Sakthivel

2022

IJERPH

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A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers.

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An Evaluation of Work Posture and Musculoskeletal Disorder Risk Level Identification for the Fireworks Industry Worker's

Cited by 6 publications

References 11 publications

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Analisis Indikasi Resiko Postur Pengguna Desain Meja Belajar Portabel

Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning

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