Xiyun Luo

dblp:433/0507 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0005-8741-5755ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input › text entry
eyes-free text entry
1.012026
AnkleType: A Hands- and Eyes-free Foot-based Text Entry Technique in Virtual Reality · CHI 2026
Interaction techniques and input
text entry
1.012026
AnkleType: A Hands- and Eyes-free Foot-based Text Entry Technique in Virtual Reality · CHI 2026
Interaction techniques and input › text entry
virtual reality text entry
1.012026
AnkleType: A Hands- and Eyes-free Foot-based Text Entry Technique in Virtual Reality · CHI 2026
Interaction techniques and input
hands-free interaction
0.312026
AnkleType: A Hands- and Eyes-free Foot-based Text Entry Technique in Virtual Reality · CHI 2026

Methods — techniques the papers use, named apart from their topics

longitudinal study · 1.0keyboard layout optimization · 1.0gesture elicitation · 1.0
YearPublicationVenuePosition
2026 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 BPSit in the visual condition, and 11.15 WPM and 12.87 WPM, respectively in the eyes-free condition. © 2026 Copyright held by the owner/author(s).
Xiyun Luo, Weirong Luo, Kening Zhu, Taizhou Chen
CHI1