AnkleType: A Hands- and Eyes-free Foot-based Text Entry Technique in Virtual Reality

Abstract

Virtual Reality (VR) emphasizes immersive experiences, while text entry often requires hands or visual attention, which may disrupt the interaction flows in VR. We present AnkleType, a hand- and eye-free text-entry technique that leverages ankle-based gestures for both standing and sitting situations. We began with two preliminary studies: one investigated the movement range of users' ankles, and the other elicited user-preferred ankle gestures for text-entry-related operations. The findings of these two studies guided our design of AnkleType. To optimize AnkleType's keyboard layout for eye-free input, we conducted a user study to capture the users' natural ankle spatial awareness with a computer-simulated language test. Through a pairwise comparison study, we designed a bipedal input strategy for sitting (BPSit) and a unipedal input strategy for standing (UPStand). Our first in-VR text-entry evaluation with 16 participants demonstrated that our methods could support the average typing speed from 8.99 WPM (BPSit) to 9.13 WPM (UPStand) for our first-time users. We further evaluated our design with a 7-day longitudinal study with twelve participants. Participants achieved an average typing speed of 15.05 WPM with UPStand and 16.70 WPM with BPSit in the visual condition, and 11.15 WPM and 12.87 WPM, respectively in the eyes-free condition.

Figures (9)

• Figure 1: Illustration of our interface design concept of using two concentric radial areas for letter and word input areas separately.
• Figure 2: Foot gesture elicitation study results. This figure shows the top 3 gesture illustrations for each referent under both unipedal and bipedal strategies with their weighted preference score. Each gesture had a maximum score of 54.
• Figure 3: ⓐThe shoe prototype for AnkleType, including two photo-reflective sensors at the fore and rear of the shoe, an ESP-32 to process the sensor signal, and an HTC Vive Tracker 2.0 to enable ankle rotation tracking. This implementation was based on Chan et al.'s chan2024seated solution.ⓑThe interface for the keyboard layout optimization study. ①The target text to be transcribed, ②The current input text, ③The initial keyboard layout, where we evenly distributed 27-key (26 letters + space bar) across the keyboard area.
• Figure 4: Distribution of foot tapping positions with 95% confidence interval in a 26-key Alphabetical-order keyboard. (a) Distribution of foot tapping positions for standing condition. (b) Distribution of foot tapping positions for sitting condition.
• Figure 5: Word disambiguation score $L^k$ and jointed spatial matching score $S_{joint}^k$ of each key number. Note that we did not include the space key when calculating these metrics. Therefore, the actual number of keys for calculating these metrics should be minus one.