Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2212.12170

Sense of Embodiment Inducement for People with Reduced Lower-body Mobility and Sensations with Partial-Visuomotor Stimulation

Hyuckjin Jang·Taehei Kim·Seo Young Oh·Jeongmi Lee·Sunghee Lee·Sang Ho Yoon
View original source ↗Read source document ↗Export metadata ↓

Abstract

To induce the Sense of Embodiment~(SoE) on the virtual 3D avatar during a Virtual Reality~(VR) walking scenario, VR interfaces have employed the visuotactile or visuomotor approaches. However, people with reduced lower-body mobility and sensation~(PRLMS) who are incapable of feeling or moving their legs would find this task extremely challenging. Here, we propose an upper-body motion tracking-based partial-visuomotor technique to induce SoE and positive feedback for PRLMS patients. We design partial-visuomotor stimulation consisting of two distinctive inputs~(\textit{Button Control} \& \textit{Upper Motion tracking}) and outputs~(\textit{wheelchair motion} \& \textit{Gait Motion}). The preliminary user study was conducted to explore subjective preference with qualitative feedback. From the qualitative study result, we observed the positive response on the partial-visuomotor regarding SoE in the asynchronous VR experience for PRLMS.

Explore related subjects

cs.HCcs.GR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hyuckjin Jang, Taehei Kim, Seo Young Oh, Jeongmi Lee, Sunghee Lee, Sang Ho Yoon. 2022-12-23. Sense of Embodiment Inducement for People with Reduced Lower-body Mobility and Sensations with Partial-Visuomotor Stimulation. https://arxiv.org/abs/2212.12170

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Explanation Navigator: Rectifying Out-of-Scope Human Interpretations of Leaky AI Explanations through Conversational Guidance

As explanations of artificial intelligence systems proliferate, their recipients must grasp not only what they convey but also recognise what they cannot. We conducted an interview study with nine participants to examine how explainees reason when their information needs exceed the scope of available explanations. Participants often unwittingly confabulated explanatory insights when relevant information was missing from the explanations, not recognising the inherent limitations thereof. We characterise such explanations as leaky explanations -- simplifications that strive to hide complexity yet whose correct interpretation hinges on understanding of the concealed details. To address out-of-scope interpretations we propose Explanation Navigator: a conversational interaction framework that detects mismatches between users' information needs and explanations' content, elucidating pertinent yet implicit details and providing complementary explanations for unmet information needs. An online study with 316 participants showed that our approach allowed explainees to recognise and rectify confabulated explanatory insights, guiding them towards developing correct understanding.

cs.HC↗
arXiv2026

Biased AI improves human performance but reduces perceived helpfulness

Artificial intelligence (AI) increasingly shapes how people think, engage, and evaluate information. To minimize risk, most current systems are designed to present as ideologically neutral with standardized output. Yet growing evidence suggests that these principles suppress cognitive engagement, impair human decision-making, and erode societal diversity. Here we test the opposite approach by deliberately injecting bias into AI assistants. In three randomized experiments with 5,000 participants, biased AI improved human performance relative to default and neutral AI in tasks ranging from misinformation evaluation and financial investment to graduate education. These gains carried a subjective cost. Participants systematically undervalued AI they believed to be biased and inflated the helpfulness of AI they believed to be neutral, regardless of the systems' actual behavior. Interacting with two AIs whose biases flanked the participant's own perspective preserved the performance gains while limiting the subjective cost and one-sided influence. Our findings reveal the strategic value of intentional bias in AI design. Rather than performing a single fair, reliable, and authoritative voice, AI that speaks from specific viewpoints triggers cognitive agency and elevates human-AI performance in judgment, decision-making, and problem-solving.

cs.HC↗
arXiv2026

Learning Password Best Practices Through In-Task Instruction

Users often make security- and privacy-relevant decisions without a clear understanding of the rules that govern safe behavior. We introduce pedagogical friction, a design approach that inserts brief, instructional interactions at the moment of action. We evaluate this approach in the context of password creation, a familiar task with clear quality criteria. We conducted a randomized study with 128 participants across four interface conditions that varied the depth and interactivity of guidance. We assessed three outcomes: (1) rule compliance in a subsequent password task without guidance, (2) accuracy on survey questions tied to password rules, and (3) behavior-knowledge alignment, which captures whether participants who correctly followed a rule also recognized it on the survey. Across the guided conditions, participants corrected most rule violations in the follow-up task and showed high behavior-knowledge alignment. Survey results suggested clearer advantages for some rule types, especially symbol related questions. These results position pedagogical friction as a lightweight intervention for security- and privacy-critical interfaces.

cs.HC↗