Incidents in manual handling activities

Lortie, Monique; Pelletier, Robert L.

doi:10.1016/0925-7535(95)00066-6

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…In these cases, workers report circumstances describing a problem with the control of the merchandise without actually mentioning a control diculty. Accident studies rarely report this issue, while on-site incident observations (Lortie and Pelletier, 1996) and interviews with dock handlers (Authier and Lortie, 1993) con®rm its importance. However, it is clear that analyzing implicit information is more dicult than classifying`readily ascertainable facts'.…”

Section: Discussionmentioning

confidence: 99%

The classification of accident data

Lortie

Rizzo

1998

Safety Science

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Section: Discussionmentioning

confidence: 99%

The classification of accident data

Lortie

Rizzo

1998

Safety Science

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“…À titre d'exemple, lors du chargement d'une remorque, c'est la planification du chargement (ordre de chargement, position des marchandises) qui limite les remanutentions. L'observation de 17 préposés au chargement a montré que de 14 % à 77 % (moyenne : 32 %) des marchandises étaient remanutentionnées (Lortie & Pelletier, 1996). Dans le cas des livreurs, c'est plutôt l'organisation du travail d'équipe, la connaissance du secteur et les relations aux clients qui jouent un rôle déterminant.…”

Section: Manutention : Information Et Décision 197unclassified

“…Dans le cas du chargement d'une remorque, ce travail devient particulièrement complexe car il faut à la fois entrer le plus de marchandises possible, réaliser un chargement stable et équilibré et tenir compte des caractéristiques de poids et de la fragilité des objets. Plus le manutentionnaire réussit à positionner adéquatement l'objet dès le premier dépôt, moins il aura de marchandises à remanutentionner (Lortie & Pelletier, 1996).…”

Section: 2 Sur La Situationunclassified

Manutention : prise d'information et décision d'action

Lortie

2002

Le Travail Humain

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Tous droits réservés pour tous pays.La reproduction ou représentation de cet article, notamment par photocopie, n'est autorisée que dans les limites des conditions générales d'utilisation du site ou, le cas échéant, des conditions générales de la licence souscrite par votre établissement. Toute autre reproduction ou représentation, en tout ou partie, sous quelque forme et de quelque manière que ce soit, est interdite sauf accord préalable et écrit de l'éditeur, en dehors des cas prévus par la législation en vigueur en France. Il est précisé que son stockage dans une base de données est également interdit.

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“…Despite technological advances and process automation, the construction industry still has many tasks that require frequent handling of various materials by workers' hands (Pan et al, 2003). Research has shown that manual material handling (MMH) tasks are significantly related to construction injuries, accounting for about one-third of construction accidents (Perttula et al, 2003), and one accident occurred every seven handling maneuvers on average (Lortie and Pelletier, 1996). Especially, performing such tasks contributes substantially to work-related musculoskeletal disorders (WMSDs) (National Institute for Occupational Safety and Health NIOSH, 2007), which are the primary cause of nonfatal occupational injuries in the construction industry.…”

Section: Introductionmentioning

confidence: 99%

Risk events recognition using smartphone and machine learning in construction workers' material handling tasks

Duan

Zhou

Tao

2022

ECAM

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PurposeThe outbreak of the pandemic makes it more difficult to manage the safety or health of construction workers in infrastructure construction. Risk events in construction workers' material handling tasks are highly relevant to workers' work-related musculoskeletal disorders. However, there are still many problems to be resolved in recognizing risk events accurately. The purpose of this research is to propose an automatic and non-invasive recognition method for construction workers in material handling tasks during the pandemic based on smartphone and machine learning.Design/methodology/approachThis research proposes a method to recognize and classify four different risk events by collecting specific acceleration and angular velocity patterns through built-in sensors of smartphones. The events were simulated with anterior handling and shoulder handling methods in the laboratory. After data segmentation and feature extraction, five different machine learning methods are used to recognize risk events and the classification performances are compared.FindingsThe classification result of the shoulder handling method was slightly better than the anterior handling method. By comparing the accuracy of five different classifiers, cross-validation results showed that the classification accuracy of the random forest algorithm was the highest (76.71% in anterior handling method and 80.13% in shoulder handling method) when the window size was 0.64 s.Originality/valueLess attention has been paid to the risk events in workers' material handling tasks in previous studies, and most events are recorded by manual observation methods. This study provided a simple and objective way to judge the risk events in manual material handling tasks of construction workers based on smartphones, which can be used as a non-invasive way for managers to improve health and labor productivity during the pandemic.

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Incidents in manual handling activities

Cited by 9 publications

References 18 publications

The classification of accident data

The classification of accident data

Manutention : prise d'information et décision d'action

Risk events recognition using smartphone and machine learning in construction workers' material handling tasks

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