Abstract

DURAN-CORONADO, Adrián Alonso; MALDONADO-MACIAS, Aidé Aracely; BARAJAS-BUSTILLOS, Manuel Alejandro  and  HERNANDEZ-ARELLANO, Juan Luis. Cognitive analyses of mental workload and human error identification for the improvement of user experience. CienciaUAT [online]. 2019, vol.14, n.1, pp.71-84.  Epub Aug 03, 2020. ISSN 2007-7858.  https://doi.org/10.29059/cienciauat.v14i1.1173.

People can sometimes feel overwhelmed when trying to interact with modern technology. Some everyday products have deficient designs which can result in an unsatisfactory experience and even frustration. Additionally, they may produce a mental workload that can induce the user to make mistakes during their usage. The use of techniques for human error identification and mental load assessment in products evaluation and design can provide relevant and useful information to improve the user’s experience. The objective of this work was to propose a methodology for integrating mental workload assessment and human error analysis into product design processes. In this work, mental workload was evaluated during the configuration of wireless earphones using the Workload Profile (WP) technique, and the Task Analysis for Error Identification (TAFEI). Ten users voluntarily participated in the study; their experiences during the use of mobile phone earphones were video recorded. Rating sheets were used to assess mental workload and human errors were identified during the earphones’ setup stage with the mobile phone. The method employed for this research offers higher sensitivity in the assessment of mental workload. It also enables the identification of attention resources that were more frequently used during task completion. Two opportunities for redesign were identified. The first one is related to the light signal detection and the second one to the correct identification of ear tips. Conclusions and recommendations are given for designers to improve the interaction between people and products.

Keywords : error prevention; mental workload; user experience; TAFEI; Workload Profile.