Investigating Cross-Modal Approaches for Evaluating Error Acceptability of a Recognition-Based Input Technique

Abstract

Emerging input techniques that rely on sensing and recognition can misinterpret a user's intention, resulting in errors and, potentially, a negative user experience. To enhance the development of such input techniques, it is valuable to understand implications of these errors, but they can very costly to simulate. Through two controlled experiments, this work explores various low-cost methods for evaluating error acceptability of freehand mid-air gestural input in virtual reality. Using a gesture-driven game and a drawing application, the first experiment elicited error characteristics through text descriptions, video demonstrations, and a touchscreen-based interactive simulation. The results revealed that video effectively conveyed the dynamics of errors, whereas the interactive modalities effectively reproduced the user experience of effort and frustration. The second experiment contrasts the interactive touchscreen simulation with the target modality - a full VR simulation - and highlights the relative costs and benefits for assessment in an alternative, but still interactive, modality. These findings introduce a spectrum of low-cost methods for evaluating recognition-based errors in VR and a series of characteristics that can be understood in each.