Cleaning in the 21st Century: The musculoskeletal disorders associated with the centuries-old occupation – A literature review

Lin, Jia‐Hua; Lee, Wonil; Smith, Caroline; Yragui, Nanette L.; Foley, Michael P.; Shin, Gwanseob

doi:10.1016/j.apergo.2022.103839

Cited by 10 publications

(5 citation statements)

References 85 publications

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“…Alguns estudos destacam a tarefa, tecnologia, organização, ambiente, indivíduo e suas interações como fatores a serem analisados e inseridos nas intervenções para redução de riscos. (Lin et al, 2022).…”

Section: Estudos Ergonômicosunclassified

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Risco ergonômico para os profissionais de limpeza em empresas terceirizadas

Santos,

Lengruber,

Silva Junior

et al. 2024

Cuad. Ed. Desar.

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Objetivo: Buscar publicações dos últimos cinco anos sobre os estudos ergonômicos de profissionais de limpeza de empresas de terceirização de serviços. Método: revisão integrativa, realizada nas bases LILACS, Scopus, PUBMED e CINAHL em novembro 2023. Os descritores “ergonomia”, “serviço de limpeza” e seus sinônimos foram combinados por operadores booleanos. Resultados: Dos 90 artigos analisados, foram escolhidos 08 para construção desta revisão. Evidenciou-se os trabalhadores que executam as atividades de limpeza possuem o mesmo perfil socioeconômico estando expostos aos riscos ergonômicos e mais suscetíveis a desenvolver lesões osteomusculares. Conclusão: Se faz necessário ampliar as pesquisas sobre os trabalhadores de limpeza e fornecer melhores condições de trabalho e ferramentas ergonômicas para mitigar os riscos existentes no ambiente de trabalho.

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Section: Estudos Ergonômicosunclassified

“…O trabalho de limpeza envolve várias tarefas e, por vezes, expõe estes trabalhadores a inumeros riscos ocupacionais e doenças osteomusculares. (Lin;Shin;Gerberich, 2023).…”

Section: Introductionunclassified

Risco ergonômico para os profissionais de limpeza em empresas terceirizadas

Santos,

Lengruber,

Silva Junior

et al. 2024

Cuad. Ed. Desar.

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“…Service robots consistently get attention to be deployed in domestic and public spaces (Wirtz et al, 2018). Among these service tasks, cleaning plays a vital role since cleaning is a repetitive task associated with shortcomings, such as scarcity of labors (Charles et al, 2009; Lin et al, 2022). Hence, in recent years cleaning has been mostly transferred to robots (Kim et al, 2019; Zhang, 2022).…”

Section: Introductionmentioning

confidence: 99%

Insights from autonomy trials of a self‐reconfigurable floor‐cleaning robot in a public food court

Wijegunawardana,

Muthugala,

Samarakoon

et al. 2024

Journal of Field Robotics

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As autonomous cleaning robots advance, we expect certain features, such as higher area coverage and robustness. To explore these features and their challenges, we need tools and strategies that allow us to develop them rapidly. In this paper, we present lessons learned and results while performing 3 months long field trials on the autonomy of a self‐reconfigurable floor‐cleaning robot. The autonomy here includes path planning and navigation of the robot for coverage in a public food court. The environment of the food court is often subjected to alterations due to human activities. One of the main contributions of this paper is proposing a task‐level execution system to deploy the cleaning robot in an actual use case scenario. We also provide in‐depth discussions on the solutions and the implementation strategies we found in accomplishing goals. These findings would be helpful in conducting autonomy development targeted for field deployments of self‐reconfigurable robots and cleaning robots.

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“…Cleaning is typically considered monotonous work mainly performed in dirty and unfavourable environments 1 . It can even be associated with dangerous objects and locations or cause cumulative trauma disorders in human labour 2 . Cleaning is an essential task for maintaining living standards.…”

Section: Introductionmentioning

confidence: 99%

Vision-based dirt distribution mapping using deep learning

Singh,

Wijegunawardana,

Samarakoon

et al. 2023

Sci Rep

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Cleaning is a fundamental routine task in human life that is now handed over to leading-edge technologies such as robotics and artificial intelligence. Various floor-cleaning robots have been developed with different cleaning functionalities, such as vacuuming and scrubbing. However, failures can occur when a robot tries to clean an incompatible dirt type. These situations will not only reduce the efficiency of the robot but also impose severe damage to the robots. Therefore, developing effective methods to classify the cleaning tasks performed in different regions and assign them to the respective cleaning agent has become a trending research domain. This article proposes a vision-based system that employs YOLOv5 and DeepSORT algorithms to detect and classify dirt to create a dirt distribution map that indicates the regions to be assigned for different cleaning requirements. This map would be useful for a collaborative cleaning framework for deploying each cleaning robot to its respective region to achieve an uninterrupted and energy-efficient operation. The proposed method can be executed with any mobile robot and on any surface and dirt, achieving high accuracy of 81.0%, for dirt indication in the dirt distribution map.

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Cleaning in the 21st Century: The musculoskeletal disorders associated with the centuries-old occupation – A literature review

Cited by 10 publications

References 85 publications

Risco ergonômico para os profissionais de limpeza em empresas terceirizadas

Risco ergonômico para os profissionais de limpeza em empresas terceirizadas

Insights from autonomy trials of a self‐reconfigurable floor‐cleaning robot in a public food court

Vision-based dirt distribution mapping using deep learning

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