Psychomotor Task Assistance and Training using Extended Reality

Abstract

Psychomotor skills consist of activities that require a combination of cognitive processing and precise motor execution. These skills are essential in many domains, including industrial work, healthcare, and other areas that require decent craftsmanship. Mastering tasks that require these skills is challenging and needs a lot of practice, due to the complexity of required movements in combination with the need for real-time decision-making. Errors made in the execution of these tasks also typically bring risks with them—especially in hazardous environments or when handling dangerous and sensitive components. Extended Reality (XR), the technology encompassing Virtual Reality (VR) and Augmented Reality (AR), presents novel opportunities for enhancing the learning and execution of psychomotor tasks through immersive training environments and real-time guidance. In this thesis, we explore how XR can improve training and execution across three categories of psychomotor skills: procedural skills, fine motor skills, and gross motor skills. In Part I, we present how VR can be used as a tool for learning procedural skills in hazardous environments. We focus on its effectiveness in both procedure communication and educational acceptance. For Part II, we address the potential of AR for guiding fine motor skills. We discuss how to guide motion speed control, a complex activity, and its trade-offs in regard to task accuracy. Finally, in Part III, we explore the role AR can take in improving gross motor skill execution by providing real-time feedback on movement patterns and task coverage. By assessing the impact XR has on training efficiency, user experience, and task performance, our main contribution concerns the design of effective XR-based psychomotor assistance tools across task measures such as procedural knowledge, speed control, and pattern adherence. We believe that our insights into how immersive technology can enhance psychomotor task execution and training pave the way for future advancements through XR-assisted tools.