The work environment influences workers' well-being and contributes to the growth of personal experiences. In fact, working in an unhealthy workplace can cause stress, frustration, and anxiety. Therefore, companies have to deal with the workers' well-being in the work environment, making the management of human factors a crucial aspect. In this context, the introduction of Industry 4.0 technologies can support workplace monitoring and improvement. Some researchers propose structured methods that consider several ergonomic domains together; however, it is necessary to create platforms that support data collection, elaboration, and correlation in an integrated way. Accordingly, this paper presents a tool that supports the monitoring of operators' activities, the data analysis, and the implementation of corrective actions to make the workplace socially sustainable. Preliminary tests were conducted to assess the functionality of the tool architecture and two use cases are presented. They focus on posture analysis and stress detection by inertial sensors and unsupervised machine learning algorithms, respectively.

Human work sustainability tool / Ciccarelli, Marianna; Papetti, Alessandra; Germani, Michele; Leone, Alessandro; Rescio, Gabriele. - In: JOURNAL OF MANUFACTURING SYSTEMS. - ISSN 0278-6125. - 62:(2022), pp. 76-86. [10.1016/j.jmsy.2021.11.011]

Human work sustainability tool

Marianna Ciccarelli;Alessandra Papetti;Michele Germani;
2022-01-01

Abstract

The work environment influences workers' well-being and contributes to the growth of personal experiences. In fact, working in an unhealthy workplace can cause stress, frustration, and anxiety. Therefore, companies have to deal with the workers' well-being in the work environment, making the management of human factors a crucial aspect. In this context, the introduction of Industry 4.0 technologies can support workplace monitoring and improvement. Some researchers propose structured methods that consider several ergonomic domains together; however, it is necessary to create platforms that support data collection, elaboration, and correlation in an integrated way. Accordingly, this paper presents a tool that supports the monitoring of operators' activities, the data analysis, and the implementation of corrective actions to make the workplace socially sustainable. Preliminary tests were conducted to assess the functionality of the tool architecture and two use cases are presented. They focus on posture analysis and stress detection by inertial sensors and unsupervised machine learning algorithms, respectively.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/316408
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