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Pireddu A, Bedini A, Lombardi M, Ciribini ALC, Berardi D. A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:831. [PMID: 39063408 PMCID: PMC11277231 DOI: 10.3390/ijerph21070831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/14/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024]
Abstract
Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail). OBJECTIVES The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors. METHODS Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis. RESULTS The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97-8.18 × 10-1; p-value 7.67 × 10-55-1.28 × 10-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84-0.47; p-value 5.79 × 10-25--3.59 × 10-6). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. CONCLUSIONS The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53-0.96) compared to not-supervised methods.
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Affiliation(s)
- Antonella Pireddu
- Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements (DIT), Italian National Institute for Insurance against Accidents at Work, Inail, 00144 Rome, Italy
| | - Angelico Bedini
- Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements (DIT), Italian National Institute for Insurance against Accidents at Work, Inail, 00144 Rome, Italy
| | - Mara Lombardi
- Department of Chemical Engineering Materials Environment (DICMA), Sapienza-University of Rome, 00184 Rome, Italy; (M.L.); (D.B.)
| | - Angelo L. C. Ciribini
- Department of Civil Engineering, Architecture, Land, Environment and Mathematics (DICATAM), Brescia University, 25121 Brescia, Italy;
| | - Davide Berardi
- Department of Chemical Engineering Materials Environment (DICMA), Sapienza-University of Rome, 00184 Rome, Italy; (M.L.); (D.B.)
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Khairuddin MZF, Sankaranarayanan S, Hasikin K, Abd Razak NA, Omar R. Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model. PeerJ Comput Sci 2024; 10:e1985. [PMID: 38660193 PMCID: PMC11042013 DOI: 10.7717/peerj-cs.1985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024]
Abstract
Background This study introduced a novel approach for predicting occupational injury severity by leveraging deep learning-based text classification techniques to analyze unstructured narratives. Unlike conventional methods that rely on structured data, our approach recognizes the richness of information within injury narrative descriptions with the aim of extracting valuable insights for improved occupational injury severity assessment. Methods Natural language processing (NLP) techniques were harnessed to preprocess the occupational injury narratives obtained from the US Occupational Safety and Health Administration (OSHA) from January 2015 to June 2023. The methodology involved meticulous preprocessing of textual narratives to standardize text and eliminate noise, followed by the innovative integration of Term Frequency-Inverse Document Frequency (TF-IDF) and Global Vector (GloVe) word embeddings for effective text representation. The proposed predictive model adopts a novel Bidirectional Long Short-Term Memory (Bi-LSTM) architecture and is further refined through model optimization, including random search hyperparameters and in-depth feature importance analysis. The optimized Bi-LSTM model has been compared and validated against other machine learning classifiers which are naïve Bayes, support vector machine, random forest, decision trees, and K-nearest neighbor. Results The proposed optimized Bi-LSTM models' superior predictability, boasted an accuracy of 0.95 for hospitalization and 0.98 for amputation cases with faster model processing times. Interestingly, the feature importance analysis revealed predictive keywords related to the causal factors of occupational injuries thereby providing valuable insights to enhance model interpretability. Conclusion Our proposed optimized Bi-LSTM model offers safety and health practitioners an effective tool to empower workplace safety proactive measures, thereby contributing to business productivity and sustainability. This study lays the foundation for further exploration of predictive analytics in the occupational safety and health domain.
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Affiliation(s)
| | - Suresh Sankaranarayanan
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Hofuf, Kingdom of Saudi Arabia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia
| | - Nasrul Anuar Abd Razak
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia
| | - Rosidah Omar
- Occupational and Environmental Health Unit, Kedah State Health Department, Alor Setar, Kedah, Malaysia
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Khairuddin MZF, Hasikin K, Razak NAA, Mohshim SA, Ibrahim SS. Harnessing the Multimodal Data Integration and Deep Learning for Occupational Injury Severity Prediction. IEEE ACCESS 2023; 11:85284-85302. [DOI: 10.1109/access.2023.3304328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
| | - Nasrul Anuar Abd Razak
- Department of Biomedical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
| | - Siti Afifah Mohshim
- Medical Engineering Technology Section, British Malaysian Institute, Universiti Kuala Lumpur, Kuala Lumpur, Selangor, Malaysia
| | - Siti Salwa Ibrahim
- Negeri Sembilan State Health Department, Ministry of Health, Seremban, Negeri Sembilan, Malaysia
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Khairuddin MZF, Lu Hui P, Hasikin K, Abd Razak NA, Lai KW, Mohd Saudi AS, Ibrahim SS. Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13962. [PMID: 36360843 PMCID: PMC9653932 DOI: 10.3390/ijerph192113962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/09/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
Forecasting the severity of occupational injuries shall be all industries' top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; 'nature of injury', 'type of event', and 'affected body part' in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance.
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Affiliation(s)
- Mohamed Zul Fadhli Khairuddin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
- Environmental Healthcare Section, Institute of Medical Science Technology, Universiti Kuala Lumpur, Kajang 40300, Selangor, Malaysia
| | - Puat Lu Hui
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
- Centre of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Nasrul Anuar Abd Razak
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Ahmad Shakir Mohd Saudi
- Centre of Water Engineering Technology, Water Energy Section, Malaysia France Institute, Universiti Kuala Lumpur, Bangi 43650, Selangor, Malaysia
| | - Siti Salwa Ibrahim
- Negeri Sembilan State Health Department, Seremban 70300, Negeri Sembilan, Malaysia
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Khairuddin MZF, Hasikin K, Abd Razak NA, Lai KW, Osman MZ, Aslan MF, Sabanci K, Azizan MM, Satapathy SC, Wu X. Predicting occupational injury causal factors using text-based analytics: A systematic review. Front Public Health 2022; 10:984099. [PMID: 36187621 PMCID: PMC9521307 DOI: 10.3389/fpubh.2022.984099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/23/2022] [Indexed: 01/25/2023] Open
Abstract
Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research.
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Affiliation(s)
- Mohamed Zul Fadhli Khairuddin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,Institute of Medical Science Technology, Universiti Kuala Lumpur, Selangor, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,Centre of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,*Correspondence: Khairunnisa Hasikin
| | - Nasrul Anuar Abd Razak
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Mohd Zamri Osman
- Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Gambang, Malaysia
| | - Muhammet Fatih Aslan
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey
| | - Kadir Sabanci
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia
| | - Suresh Chandra Satapathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | - Xiang Wu
- School of Medical Information and Engineering, Xuzhou Medical University Xuzhou, Xuzhou, Jiangsu, China,Xiang Wu
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Multimodal Classification of Safety-Report Observations. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Modern businesses are obligated to conform to regulations to prevent physical injuries and ill health for anyone present on a site under their responsibility, such as customers, employees and visitors. Safety officers (SOs) are engineers, who perform site audits to businesses, record observations regarding possible safety issues and make appropriate recommendations. In this work, we develop a multimodal machine-learning architecture for the analysis and categorization of safety observations, given textual descriptions and images taken from the location sites. For this, we utilize a new multimodal dataset, Safety4All, which contains 5344 safety-related observations created by 86 SOs in 486 sites. An observation consists of a short issue description, written by the SOs, accompanied with images where the issue is shown, relevant metadata and a priority score. Our proposed architecture is based on the joint fine tuning of large pretrained language and image neural network models. Specifically, we propose the use of a joint task and contrastive loss, which aligns the text and vision representations in a joint multimodal space. The contrastive loss ensures that inter-modality representation distances are maintained, so that vision and language representations for similar samples are close in the shared multimodal space. We evaluate the proposed model on three tasks, namely, priority classification of input observations, observation assessment and observation categorization. Our experiments show that inspection scene images and textual descriptions provide complementary information, signifying the importance of both modalities. Furthermore, the use of the joint contrastive loss produces strong multimodal representations and outperforms a baseline simple model in tasks fusion. In addition, we train and release a large transformer-based language model for the Greek language based on the Electra architecture.
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Tamascelli N, Solini R, Paltrinieri N, Cozzani V. Learning from major accidents: A machine learning approach. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107786] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Shkembi A, Smith LM, Neitzel RL. Retrospective assessment of the association between noise exposure and nonfatal and fatal injury rates among miners in the United States from 1983 to 2014. Am J Ind Med 2022; 65:30-40. [PMID: 34706100 DOI: 10.1002/ajim.23305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/11/2021] [Accepted: 10/12/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND Mining is a significant economic force in the United States but has historically had among the highest nonfatal injury rates across all industries. Several factors, including workplace hazards and psychosocial stressors, may increase injury and fatality risk. Mining is one of the noisiest industries; however, the association between injury risk and noise exposure has not been evaluated in this industry. In this ecological study, we assessed the association between noise exposure and nonfatal and fatal occupational injury rates among miners. METHODS Federal US mining accident, injury, and illness data sets from 1983 to 2014 were combined with federal quarterly mining employment and production reports to quantify annual industry rates of nonfatal injuries and fatalities. An existing job-exposure matrix for occupational noise was used to estimate annual industry time-weighted average (TWA, dBA) exposures. Negative binomial models were used to assess relationships between noise, hearing conservation program (HCP) regulation changes in 2000, year, and mine type with incidence rates of injuries and fatalities. RESULTS Noise, HCP regulation changes, and mine type were each independently associated with nonfatal injuries and fatalities. In multivariate analysis, each doubling (5 dB increase) of TWA was associated with 1.08 (95% confidence interval: 1.05, 1.11) and 1.48 (1.23, 1.78) times higher rate of nonfatal injuries and fatalities, respectively. HCP regulation changes were associated with 0.61 (0.54, 0.70) and 0.49 (0.34, 0.71) times lower nonfatal injury and fatality rates, respectively. CONCLUSION Noise may be a significant independent risk factor for injuries and fatalities in mining.
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Affiliation(s)
- Abas Shkembi
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Lauren M Smith
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Richard L Neitzel
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
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Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace-A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182211891. [PMID: 34831645 PMCID: PMC8621458 DOI: 10.3390/ijerph182211891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 10/19/2021] [Accepted: 11/02/2021] [Indexed: 01/29/2023]
Abstract
Non-pathological mental fatigue is a recurring, but undesirable condition among people in the fields of office work, industry, and education. This type of mental fatigue can often lead to negative outcomes, such as performance reduction and cognitive impairment in education; loss of focus and burnout syndrome in office work; and accidents leading to injuries or death in the transportation and manufacturing industries. Reliable mental fatigue assessment tools are promising in the improvement of performance, mental health and safety of students and workers, and at the same time, in the reduction of risks, accidents and the associated economic loss (e.g., medical fees and equipment reparations). The analysis of biometric (brain, cardiac, skin conductance) signals has proven to be effective in discerning different stages of mental fatigue; however, many of the reported studies in the literature involve the use of long fatigue-inducing tests and subject-specific models in their methodologies. Recent trends in the modeling of mental fatigue suggest the usage of non subject-specific (general) classifiers and a time reduction of calibration procedures and experimental setups. In this study, the evaluation of a fast and short-calibration mental fatigue assessment tool based on biometric signals and inter-subject modeling, using multiple linear regression, is presented. The proposed tool does not require fatigue-inducing tests, which allows fast setup and implementation. Electroencephalography, photopletismography, electrodermal activity, and skin temperature from 17 subjects were recorded, using an OpenBCI helmet and an Empatica E4 wristband. Correlations to self-reported mental fatigue levels (using the fatigue assessment scale) were calculated to find the best mental fatigue predictors. Three-class mental fatigue models were evaluated, and the best model obtained an accuracy of 88% using three features, β/θ (C3), and the α/θ (O2 and C3) ratios, from one minute of electroencephalography measurements. The results from this pilot study show the feasibility and potential of short-calibration procedures and inter-subject classifiers in mental fatigue modeling, and will contribute to the use of wearable devices for the development of tools oriented to the well-being of workers and students, and also in daily living activities.
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Iordache AMM, Dura CC, Coculescu C, Isac C, Preda A. Using Neural Networks in Order to Analyze Telework Adaptability across the European Union Countries: A Case Study of the Most Relevant Scenarios to Occur in Romania. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010586. [PMID: 34682333 PMCID: PMC8535855 DOI: 10.3390/ijerph182010586] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/03/2021] [Accepted: 10/04/2021] [Indexed: 11/23/2022]
Abstract
Our study addresses the issue of telework adoption by countries in the European Union and draws up a few feasible scenarios aimed at improving telework’s degree of adaptability in Romania. We employed the dataset from the 2020 Eurofound survey on Living, Working and COVID-19 (Round 2) in order to extract ten relevant determinants of teleworking on the basis of 24,123 valid answers provided by respondents aged 18 and over: the availability of work equipment; the degree of satisfaction with the experience of working from home; the risks related to potential contamination with SARS-CoV-2 virus; the employees’ openness to adhering to working-from-home patterns; the possibility of maintaining work–life balance objectives while teleworking; the level of satisfaction on the amount and the quality of work submitted, etc. Our methodology entailed the employment of SAS Enterprise Guide software to perform a cluster analysis resulting in a preliminary classification of the EU countries with respect to the degree that they have been able to adapt to telework. Further on, in order to refine this taxonomy, a multilayer perceptron neural network with ten input variables in the initial layer, six neurons in the intermediate layer, and three neurons in the final layer was successfully trained. The results of our research demonstrate the existence of significant disparities in terms of telework adaptability, such as: low to moderate levels of adaptability (detected in countries such as Greece, Croatia, Portugal, Spain, Lithuania, Latvia, Poland, Italy); fair levels of adaptability (encountered in France, Slovakia, the Czech Republic, Hungary, Slovenia, or Romania); and high levels of adaptability (exhibited by intensely digitalized economies such Denmark, Sweden, Finland, Germany, Ireland, the Netherlands, Belgium, etc.).
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Affiliation(s)
- Ana Maria Mihaela Iordache
- Department of Informatics, Statistics and Mathematics, Romanian–American University, 012101 Bucharest, Romania;
- Correspondence: (A.M.M.I.); (C.C.D.)
| | - Codruța Cornelia Dura
- Department of Economics, University of Petroșani, 332006 Petroșani, Romania; (C.I.); (A.P.)
- Correspondence: (A.M.M.I.); (C.C.D.)
| | - Cristina Coculescu
- Department of Informatics, Statistics and Mathematics, Romanian–American University, 012101 Bucharest, Romania;
| | - Claudia Isac
- Department of Economics, University of Petroșani, 332006 Petroșani, Romania; (C.I.); (A.P.)
| | - Ana Preda
- Department of Economics, University of Petroșani, 332006 Petroșani, Romania; (C.I.); (A.P.)
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Pishgar M, Issa SF, Sietsema M, Pratap P, Darabi H. REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18136705. [PMID: 34206378 PMCID: PMC8296875 DOI: 10.3390/ijerph18136705] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/09/2021] [Accepted: 06/15/2021] [Indexed: 01/04/2023]
Abstract
Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.
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Affiliation(s)
- Maryam Pishgar
- Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60609, USA;
| | - Salah Fuad Issa
- Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;
| | - Margaret Sietsema
- Environmental and Occupational Health Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA; (M.S.); (P.P.)
| | - Preethi Pratap
- Environmental and Occupational Health Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA; (M.S.); (P.P.)
| | - Houshang Darabi
- Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60609, USA;
- Correspondence:
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