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. 2024 Aug 23;24(17):5473.
doi: 10.3390/s24175473.

Workplace Well-Being in Industry 5.0: A Worker-Centered Systematic Review

Affiliations

Workplace Well-Being in Industry 5.0: A Worker-Centered Systematic Review

Francesca Giada Antonaci et al. Sensors (Basel). .

Abstract

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart, digital, and connected industry, where well-being is key to enhance productivity, optimize man-machine interaction and guarantee workers' safety. This work aims to conduct a systematic review of current methodologies for monitoring and analyzing physical and cognitive ergonomics. Three research questions are addressed: (1) which technologies are used to assess the physical and cognitive well-being of workers in the workplace, (2) how the acquired data are processed, and (3) what purpose this well-being is evaluated for. This way, individual factors within the holistic assessment of worker well-being are highlighted, and information is provided synthetically. The analysis was conducted following the PRISMA 2020 statement guidelines. From the sixty-five articles collected, the most adopted (1) technological solutions, (2) parameters, and (3) data analysis and processing were identified. Wearable inertial measurement units and RGB-D cameras are the most prevalent devices used for physical monitoring; in the cognitive ergonomics, and cardiac activity is the most adopted physiological parameter. Furthermore, insights on practical issues and future developments are provided. Future research should focus on developing multi-modal systems that combine these aspects with particular emphasis on their practical application in real industrial settings.

Keywords: cognitive ergonomics; human–machine interaction; human–robot interaction; industry 5.0; physical ergonomics; workplace well-being.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA flowchart.
Figure 2
Figure 2
Number of occurrences of ergonomic physical indexes.
Figure 3
Figure 3
Amount of surveyed papers over years.
Figure 4
Figure 4
Adoption of the different devices in the time span covered by the review.
Figure 5
Figure 5
Graphical representation of the frequency of adoption of each of the physiological parameters.
Figure 6
Figure 6
Adoption of the different technologies to acquire physiological data in the time span considered.
Figure 7
Figure 7
Graphical representation of the frequency of adoption of the data acquisition technologies.
Figure 8
Figure 8
Graphical representation of the frequency of adoption of single vs. multiple physiological measurements.
Figure 9
Figure 9
Graphical representation of the frequency of adoption of statistical vs. machine/deep learning approaches.

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