Digitalization of the ergonomic assessment worksheet – User requirements for EAWS digital evaluation functions

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Abstract

Background: The Ergonomic Assessment Worksheet method (EAWS) is used to assess biomechanical risks for the whole body and upper extremities and to develop ergonomic measures. Different EAWS software tools are available but are mostly limited to the presentation of EAWS points and the involved strain factors. That limits the deployment of the EAWS method in practical use. Objective: For the purpose of user oriented extension of digital EAWS evaluations, requirements and needs of users have to be determined for EAWS evaluation functions and design elements as well as the user context as part of a user-centered design process. Method: The user context and requirements is investigated by using an online survey and semi-standardized interviews. The target group are EAWS-experienced people to receive more detailed information about the usage and design requirements. Results: The online survey was filled out by 61 people and 13 interviews were conducted. The EAWS is not only applied for the evaluation of working processes, but it is also increasingly used for the deduction of measures and in other cases. In general, a high importance for an EAWS digitalization can be stated. There is a particularly high need for evaluation functions such as ‘ergonomic map’, ‘sensitivity analysis’, ‘design analysis’ and ‘job rotation analysis’. Users deployed certain requirements for the design of these functions such as the presentation of essential information or the use of color coding. Conclusion: The surveys demonstrate that the EAWS method is widely used within companies, but existing digitalizations of EAWS have to be extended by further useful functions. For that purpose, the results of the two surveys can be used to develop more user-friendly EAWS evaluations.

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Spitzhirn, M., Kuhlang, P., & Bullinger, A. C. (2019). Digitalization of the ergonomic assessment worksheet – User requirements for EAWS digital evaluation functions. In Advances in Intelligent Systems and Computing (Vol. 824, pp. 272–282). Springer Verlag. https://doi.org/10.1007/978-3-319-96071-5_29

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