A Deep-Learning Based Worker’s Pose Estimation

Paudel, Prabesh; Choi, Kyoung-Ho

doi:10.1007/978-981-15-4818-5_10

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…In another study, Paudel and Choi (2020) also show the advantage of integrating promising methods to increase the reliability and performance of conventional risk assessment methods. They use the positions of the workers as input and estimate the body angles for postures to see whether the position is ergonomically safe or not.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed integrated method plays a role as an early warning system by considering risky positions and presenting immediate output to analysts or decision-makers. In addition, few studies integrate image processing and different artificial intelligence methods with ergonomic assessment tools like REBA or RULA (Chatzis et al, 2022;Estrada-Lugo et al, 2022;Paudel and Choi, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

Kavus

Tas

Gümüş

2023

Engineering Applications of Artificial Intelligence

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

Kavus

Tas

Gümüş

2023

Engineering Applications of Artificial Intelligence

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“…[18] can ergonomically analyze the actions of a single human but cannot differentiate between people and recognize human-object interactions. [19] determine the risk factor of a human's pose but only looks at a single frame rather than a period of time. Our proposed method is the first to generalize unsafe activity detection to multiple people and/or objects over an extended period of time using pose estimation and action classification.…”

Section: Related Workmentioning

confidence: 99%

An Industrial Workplace Alerting and Monitoring Platform to Prevent Workplace Injury and Accidents

Adhikesaven¹

2022

Preprint

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution. Problem and MotivationEvery year, almost two million people die from work-related accidents or diseases [1], and another 317 million suffer from work-related injuries [2]. The economic cost of these illnesses, injuries, and deaths is nearly three trillion dollars annually [3]. Beyond the direct toll on affected workers and medical expenses, workplace injuries lead to loss of productivity for all involved in the accident, property damage, and ultimately worsening morale among all workers [4]. Accounting for 39 percent of work related injuries, overexertion is the most common cause of these injuries [5]. Overexertion consists of lifting, lower, and other repetitive actions, which are all ergonomic issues. Another 31 percent of workplace injuries comes from contact with other objects [5], making the use of personal protective equipment (PPE) critical.Climate change has a strong impact on worker safety and health. Industrial workers are often most adversely affected by climate change and are exposed to physically demanding work for a longer period of time [6,7]. Both outdoor and indoor factory workers face the impacts of increased heat [6,7], which is often worsened by the lack of air conditioning and ventilation in many factories [8]. As global warming increases due to climate change, the impacts of extreme heat lead to many workplace injuries by lowering worker concentration and increasing fatigue [9]. For example, extreme heat has caused more than 20 thousands additional workplace injuries annually in California alone [9]. Overall, environmental heat causes over 170 thousand work-related injuries in the United States annually, making it the third highest cause of worker injuries [10]. It also contributes to as much as 2,000 deaths annually, possibly making heat exposure the leading cause of worker deaths [10]. Given the severe impact of climate change on workers, it is important to improve workplace safety and protect workers ...

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Automatic real-time occupational posture evaluation and select corresponding ergonomic assessments

Lin

Chen

et al. 2022

Sci Rep

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The objective is to develop a system to automatically select the corresponding assessment scales and calculate the score of the risk based on the joint angle information obtained from the imaged process (OpenPose) via image-based motion capture technology. Current occupational assessments, for example, REBA, RULA, and OWAS were used to evaluate the risk of musculoskeletal disorders. However, the assessment result would not be reported immediately. Introducing real-time occupational assessments in different working environments will be helpful for occupational injury prevention. In this study, the decision tree was developed to select the most appropriate assessment method according to the joint angles derived by OpenPose image process. Fifteen operation videos were tested and these videos can be classified into six types including maintenance, handling, assembly, cleaning, office work, and driving. The selected ergonomic assessment method by our developed decision tree in each condition are consistent with the recommendation of the Labour Research Institute. Moreover, the high-risk posture could be identified immediately and provide to the inspector for further evaluation on this posture rather than the whole operation period. This approach provides a quick inspection of the operation movements to prevent musculoskeletal injuries and enhances the application of the scale assessment method in different industrial environments.

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A Deep-Learning Based Worker’s Pose Estimation

Cited by 8 publications

References 25 publications

A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

An Industrial Workplace Alerting and Monitoring Platform to Prevent Workplace Injury and Accidents

Automatic real-time occupational posture evaluation and select corresponding ergonomic assessments

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