Abstract
Occupational diseases and specifically musculoskeletal disorders are great issues even in modern factories. Consequences (i.e. occupational diseases or generally lower worker performance) cost a lot of financial and mental funds of employees and employers. The correct way is the prevention using ergonomic analyses of workplaces. We have developed a unique methodological base using modern technologies for an effective and efficient way for quick and precise validation of a particular workplace. The procedure can also be used for employee training. We involve virtual reality (next VR) headset, motion capture, and self-developed VR software (next SW). The validation can be also performed for a virtual (artificial) workplace, thus the proposed technological framework can be used also for learning purposed. We have also proposed a method of validation. We have limited the original research on a representative assembly station and the use of the Perception Neuron suit. This paper extends the original paper. In this research, we are using a different motion capture suit TEA CAPTIV in the scope of the same experimental methods, conditions, and limitations as in the original study. The result is extrapolated to the scope of the full working shift and then compared with the original results.
Using of a different Motion Capture suit is reliable, but there are aspects that still needs deeper research. The process of measuring and software processing differs. Those issues are discussed in the paper.
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Funding
This work was supported by the Internal Science Foundation of the University of West Bohemia under Grant SGS-2021–028 ‘Developmental and Training Tools for the Interaction of Man and the Cyber–Physical Production System’.
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Krákora, D., Kubr, J., Hořejší, P., Kačerová, I., Bureš, M. (2023). Exploring Reliability of TEA CAPTIV Motion Capture Suit for a Virtual Reality Workplace Ergonomic Design. In: Visvizi, A., Troisi, O., Grimaldi, M. (eds) Research and Innovation Forum 2022. RIIFORUM 2022. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-19560-0_69
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