ClenchClick: Hands-Free Target Selection Method Leveraging Teeth-Clench for Augmented Reality

Abstract

We propose to explore teeth-clenching-based target selection in Augmented Reality (AR), as the subtlety in the interaction can be beneficial to applications occupying the user’s hand or that are sensitive to social norms. To support the investigation, we implemented an EMG-based teeth-clenching detection system (ClenchClick), where we adopted customized thresholds for different users. We first explored and compared the potential interaction design leveraging head movements and teeth clenching in combination. We finalized the interaction to take the form of a Point-and-Click manner with clenches as the confirmation mechanism. We evaluated the taskload and performance of ClenchClick by comparing it with two baseline methods in target selection tasks. Results showed that ClenchClick outperformed hand gestures in workload, physical load, accuracy and speed, and outperformed dwell in work load and temporal load. Lastly, through user studies, we demonstrated the advantage of ClenchClick in real-world tasks, including efficient and accurate hands-free target selection, natural and unobtrusive interaction in public, and robust head gesture input. investigated the interaction design, user experience in target selection tasks, and user performance in real-world tasks in a series of user studies. In our first user study, we explored nine potential designs and compared the three most promising designs (ClenchClick, ClenchCross-ingTarget, ClenchCrossingEdge) with a hand-based (Hand Gesture) and a hands-free (Dwell) baseline in target selection tasks. ClenchClick had the best overall user experience with the lowest workload. It outperformed Hand Gesture in both physical and temporal load, and outperformed Dwell in temporal and mental load. In the second study, we evaluated the performance of ClenchClick with two detection methods (General and Personalized), in comparison with a hand-based (Hand Gesture) and a hands-free (Dwell) baseline. Results showed that ClenchClick outperformed Hand Gesture in accuracy (98.9% v.s. 89.4%), and was comparable with Dwell in accuracy and efficiency. We further investigated users’ behavioral characteristics by analyzing their cursor trajectories in the tasks, which showed that ClenchClick was a smoother target selection method. It was more psychologically friendly and occupied less of the user’s attention. Finally, we conducted user studies in three real-world tasks which supported hands-free, social-friendly, and head gesture interaction. Results revealed that ClenchClick is an efficient and accurate target selection method when both hands are occupied. It is social-friendly and satisfying when performing in public, and can serve as activation to head gestures which significantly alleviates false positive issues.