Risk Assessment of Work-Related Musculoskeletal Disorders in Construction: State-of-the-Art Review

Wang, Di; Dai, Fei; Ning, Xiaopeng

doi:10.1061/(asce)co.1943-7862.0000979

Cited by 199 publications

(112 citation statements)

References 81 publications

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“…Two major tasks were established to implement the objective. First, the temporal segment networks (TSNs) (Wang et al., ) were used to recognize workers’ actions, and a new fusion strategy was proposed to consider the characteristics of far‐field surveillance videos. The experimental results show that the deep action recognition method with the new fusion strategy has achieved the comparable performance on the far‐field surveillance data set in contrast to the original method on the public data sets.…”

Section: Scope and Objectivementioning

confidence: 99%

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Capturing and Understanding Workers’ Activities in Far‐Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning

Luo

Yang

et al. 2018

Computer aided Civil Eng

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Recording workers’ activities is an important, but burdensome, management task for site supervisors. The last decade has seen a growing trend toward vision‐based activity recognition. However, recognizing workers’ activities in far‐field surveillance videos is understudied. This study proposes a hierarchical statistical method for recognizing workers’ activities in far‐field surveillance videos. The method consists of two steps. First, a deep action recognition method was used to recognize workers’ actions, and a new fusion strategy was proposed to consider the characteristics of far‐field surveillance videos. The deep action recognition method with the new fusion strategy has achieved the comparable performance (0.84 average accuracy) on the far‐field surveillance data set in contrast to the original method on the public data sets. Second, a Bayesian nonparametric hidden semi‐Markov model was innovatively used to model and infer workers’ activities based on action sequences. The latent states of the fitted Bayesian model captured workers’ activities in terms of state duration distributions and state transition distributions, which are indispensable for understanding workers’ time allocation. It has been preliminarily illustrated that the activity information learned by the Bayesian model possesses the potential to implement objective work sampling, personal physical fatigue monitoring, trade‐level health risk assessment, and process‐based quality control. Also, the limitations of this study are discussed.

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Section: Scope and Objectivementioning

confidence: 99%

“…Wang et al. () introduced TSNs to tackle the problem of action recognition in untrimmed videos. Zhang et al.…”

Section: Related Workmentioning

confidence: 99%

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Capturing and Understanding Workers’ Activities in Far‐Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning

Luo

Yang

et al. 2018

Computer aided Civil Eng

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“…EMG uses indicators of fatigue such as changes in heart rate, changes in body temperature, changes in breathing rate, changes in visual ability and changes in muscle strength, to predict level of fatigue [8,15]. Measuring the level of worker fatigue, can interfere the activities of the workers.…”

Section: Methods For Measuring Work Fatiguementioning

confidence: 99%

Construction Worker Fatigue Prediction Model Based on System Dynamic

Adi

Ratnawinanda

2017

MATEC Web Conf.

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Abstract. Construction accident can be caused by internal and external factors such as worker fatigue and unsafe project environment. Tight schedule of construction project forcing construction worker to work overtime in long period. This situation leads to worker fatigue. This paper proposes a model to predict construction worker fatigue based on system dynamic (SD). System dynamic is used to represent correlation among internal and external factors and to simulate level of worker fatigue. To validate the model, 93 construction workers whom worked in a high rise building construction projects, were used as case study. The result shows that excessive workload, working elevation and age, are the main factors lead to construction worker fatigue. Simulation result also shows that these factors can increase worker fatigue level to 21.2% times compared to normal condition. Beside predicting worker fatigue level this model can also be used as early warning system to prevent construction worker accident

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“…As a result, WMSDs not only bring damage to the workers' health, but affect the quality of life as well as work efficiency. The incidence rate occurred in back is much higher than other parts of the body [3]. Statistical data and research have revealed that bad posture is the main factor which lead to WMSDs, especially the pain of low back.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Construction Workers’ Trunk Posture Using Mobile Sensor

Zhang¹,

Bai²,

Zhao³

2017

dtetr

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Abstract. The construction industry is one of the industries with higher incidence of Work-related Musculoskeletal Disorder (WMSDs), which will have an impact on the health and life expectancy of construction workers, reduce production efficiency and cause significant economic losses. The prevalence of low back pain for construction workers is far higher than that in other parts of body, leading to the main factors of lower back pain including the awkward posture and the holding time for the same work posture. In this paper, a approach which using the built-in smartphone orientation sensor to collect data utilized to calculate the flexion-extension angle, lateral bending angle and rotation angle of the trunk is presented to identify and assess the risk of posture according to the relationship between the static angle and the holding time given by ISO11226: 2000. And the proposed method is verified by the simple working posture in laboratory. Results indicates that measurements of trunk flexions of a worker by smartphone sensor data are close to corresponding observation values and can identify and assess the risk factors that cause the low back pain of the construction workers. In addition the proposed is applicable for the WMSDs prevention, construction site management and worker training.

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Risk Assessment of Work-Related Musculoskeletal Disorders in Construction: State-of-the-Art Review

Cited by 199 publications

References 81 publications

Capturing and Understanding Workers’ Activities in Far‐Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning

Capturing and Understanding Workers’ Activities in Far‐Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning

Construction Worker Fatigue Prediction Model Based on System Dynamic

Risk Assessment of Construction Workers’ Trunk Posture Using Mobile Sensor

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