Embedded vs. Situated: An Evaluation of AR Facial Training Feedback

TL;DR

This study compares three AR feedback methods for facial training, finding that embedded feedback reduces cognitive load but can cause self-consciousness, while situated feedback improves accuracy, highlighting trade-offs for design.

Contribution

It introduces and evaluates three novel AR feedback approaches for facial exercises, revealing their respective advantages and challenges in training contexts.

Findings

Embedded feedback reduces cognitive load and is preferred by users.

Situated feedback improves exercise accuracy and correction precision.

Design trade-offs include comfort versus effectiveness in AR facial training.

Abstract

While augmented reality (AR) research demonstrates benefits of embedded visualizations for gross motor training, its applicability to facial exercises remains under-explored. Providing effective real-time feedback for facial muscle training presents unique design challenges, given the complexity of facial musculature. We developed three AR feedback approaches varying in spatial relationship to the user: situated (screen-fixed), proxy-embedded (on a mannequin), and fully embedded (overlaid on the user's face). In a within-subjects study (N=24), we measured exercise accuracy, cognitive load, and user preference during facial training tasks. The embedded feedback reduced cognitive load and received higher preference ratings, while the situated feedback enabled more precise corrections and higher accuracy. Qualitative analysis revealed a key design tension: embedded feedback improved experience but created self-consciousness and interpretive difficulty. We distill these insights into design considerations addressing the trade-offs for facial training systems, with implications for rehabilitation, performance training, and motor skill acquisition.