Error Identification and Mitigation Analysis Method Using HFACS on VR HMD

Human error has long been recognized as a key factor influencing safety, performance, and efficiency in complex tasks, including those involving advanced technologies. In the context of Virtual Reality (VR) Head-Mounted Displays (HMDs), which are increasingly applied in fields such as aviation maintenance, the presence of human errors can compromise both user experience and operational reliability. This paper presents a comprehensive method for error identification and mitigation by integrating the Human Factors Analysis and Classification System (HFACS) with VR HMD technology. Focusing on the air conditioning maintenance task of the A320 aircraft, a sample of 25 maintenance personnel participated in VR based training sessions. Quantitative metrics, including error reduction rates and task completion times, were collected across multiple attempts. Initial mean completion time was approximately 10 minutes, and by the second attempt, all participants completed the maintenance in under 10 minutes (p<0.05), reflecting a statistically significant improvement in efficiency and reduced human errors. These data, analyzed through t-tests and supported by user surveys and expert observations, validate the effectiveness and practical utility of the proposed method. The findings underscore the critical role of integrating human factors engineering with VR technology to enhance operational safety, procedural adherence, and user competence. This interdisciplinary approach not only benefits the aviation industry but also extends its implications to other high-reliability sectors leveraging VR technologies. Additionally, the study addresses the limitations posed by the rapidly evolving nature of VR systems and suggests future research directions to further optimize error mitigation strategies. Ultimately, this work contributes to the multidisciplinary advancement of VR applications, promoting safer and more efficient use of VR HMDs across various fields.