Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers

Antwi‐Afari, Maxwell Fordjour; Li, Heng; Edwards, David John; Pärn, Erika; Seo, Jongbum; Wong, Arnold Y. L.

doi:10.1016/j.autcon.2017.07.007

Cited by 164 publications

(135 citation statements)

References 71 publications

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“…This can cause microtrauma to muscles and soft tissues to accumulate, leading to permanent damage. Injuries caused on the muscles, tendons, and the skeletal system in general due to work-related conditions are collectively called work-related musculoskeletal disorders (WMSDs) and they are prevalent across a variety of occupations (Antwi-Afari et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…RULA is focused on the joint angles of the torso, neck, shoulders, elbow, and wrist positions of the most common postures, and whether the person is standing up during a task. However, RULA is subjective to the observer's judgment (Antwi-Afari et al 2017) and the most common posture might be difficult to identify (Dockrell et al 2012). Despite that, it is very popular because it does not require specialized equipment or an extremely skilled observer while it does not disrupt the working environment and it's very fast.…”

Section: Introductionmentioning

confidence: 99%

“…MoCap is not the only method that has been used to monitor human motion for ergonomic assessment. Antwi-Afari et al (Antwi-Afari et al 2017) used surface electro-myograph (sEMG) to collect the muscle activation signals. Using sEMGs, it was possible to identify which muscles were affected the most and were fatigued when lifting a weight.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment

Menychtas

Glushkova

Manitsaris

2020

J Ambient Intell Human Comput

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During an eight-hour shift, an industrial worker will inevitably cycle through specific postures. Those postures can cause microtrauma on the musculoskeletal system that accumulates, which in turn can lead to chronic injury. To assess how problematic a posture is, the rapid upper limb assessment (RULA) scoring system is widely employed by the industry. Even though it is a very quick and efficient method of assessment, RULA is not a biomechanics-based measurement that is anchored in a physical parameter of the human body. As such RULA does not give a detailed description of the impact each posture has on the human joints but rather, an overarching, simplified assessment of a posture. To address this issue, this paper proposes the use of joint angles and torques as an alternative way of ergonomics evaluation. The cumulative motion and torque throughout a trial is compared with the average motions and torques for the same task. This allows the evaluation of each joint's kinematic and kinetic performance while still be able to assess a task"at-a-glance". To do this, an upper human body model was created and the mass of each segment were assigned. The joint torques and the RULA scores were calculated for simple range of motion (ROM) tasks, as well as actual tasks from a TV assembly line. The joint angles and torques series were integrated and then normalized to give the kinematic and kinetic contribution of each joint during a task as a percentage. This made possible to examine each joint's strain during each task as well as highlight joints that need to be more closely examined. Results show how the joint angles and torques can identify which joint is moving more and which one is under the most strain during a task. It was also possible to compare the performance of a task with the average performance and identify deviations that may imply improper execution. Even though the RULA is a very fast and concise assessment tool, it leaves little room for further analyses. However, the proposed work suggests a richer alternative without sacrificing the benefit of a quick evaluation. The biggest limitation of this work is that a pool of proper executions needs to be recorded for each task before individual comparisons can be done.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment

Menychtas

Glushkova

Manitsaris

2020

J Ambient Intell Human Comput

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show abstract

“…The assumption of non-neutral postures during working activities is the main cause of injuries and accidents in a workplace [1][2][3][4][5][6]. Manual material handing (MMH) tasks expose workers to a high level of ergonomic hazards, which finds high correlation with the onset of work-related musculoskeletal disorders (WMSDs) [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…The machine learning algorithm allows for an automatic recognition of dangerous situations in several fields, such as rehabilitation and ergonomics [44][45][46][47][48][49], as well as the improvement of the adaptability of prosthetic lower limbs, as in a study by study Sun et al [50]. Thus, hazard lifting and fall risk could be immediately identified, thus increasing the safety on working places [1,38]. In 2019, Antwi-Afari and colleagues evaluated working activity recognition through four supervised machine-learning classifiers that were fed with wearable insole pressure data [51].…”

Section: Introductionmentioning

confidence: 99%

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Conforti

Mileti

Prete

et al. 2020

Sensors

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Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

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Impact of COVID‐19 on health and safety in the construction sector

Stiles

Golightly

Ryan

2021

Hum Ftrs & Erg Mfg Svc

101

120

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Construction has been significantly affected by COVID‐19 yet is critical to the post‐COVID economic recovery. Specifically, construction needs to be constantly aware of safety and risk balanced with timely project delivery. Guidance for COVID‐19 must therefore be implemented in a way that reflects working practice and pressures. There is, however, a potential knowledge gap regarding the practical feasibility and impact of applying COVID‐19 measures within construction, made more difficult by factors such as the temporary nature of projects and complex working arrangements. This article presents a commentary on safe construction during, and beyond, COVID‐19, covering the human factors challenges and practicalities of implementing COVID‐19 measures. We observe that while guidance is strong on risk management, understanding of how best to implement this guidance is not yet stable. Also, care must be taken that implementing guidance does not detract from general safety, which is also challenged by increased pressures on delivery arising from COVID‐19. There may, however, be opportunities for safer working practice arising from new awareness of health, hygiene, and safety risk. The role of safety leadership is overlooked in guidance yet is vital to ensure safe application of COVID‐19 working practices. The key message is that COVID‐19 needs to be integrated and promoted within a general risk management approach, in part because this takes account of differing priorities regarding safety risks, rather than overly focussing on COVID‐19, and also because the effectiveness of COVID‐19 mitigations can be amplified by integration with pre‐existing safety processes.

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Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers

Cited by 164 publications

References 71 publications

Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment

Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Impact of COVID‐19 on health and safety in the construction sector

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