William H. Seiple

dblp:193/6285 · DBLP profile ↗
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4ranked-venue papers
0as first author
4since 2021 · last 2026
0000-0002-5750-650XORCID · verified

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

Human-computer interaction and ubiquitous computing · 4 · 4 since 2021
YearPublicationVenuePosition
2026 From Selfie Stick to Virtual Cane: Enabling Blind Exploration through Mobile Virtual Reality
abstract
Conventional VR experiences rely heavily on visual input, creating barriers for individuals who are blind or have low vision (BLV). We present a mobile VR system that enables BLV users to explore virtual environments independently using a smartphone as a simulated white cane, augmented with spatial audio and haptic feedback. Built on ubiquitous consumer devices, the system supports embodied exploration and encourages physical movement, with multimodal feedback to help users transfer real-world cane techniques into virtual spaces and build mental maps. A user study shows that BLV participants can independently navigate complex virtual environments using familiar cane strategies. We conclude with design recommendations for accessible mobile VR systems informed by our findings.
Hao Tang 0011, Zhenchao Xia, William H. Seiple, Zhigang Zhu 0001
CHI5
2025 Enabling Auto-Correction on Soft Braille Keyboard
Dan Zhang 0021, Yan Ma 0006, Glenn Dausch, William H. Seiple, Xianfeng Gu, I. V. Ramakrishnan, Xiaojun Bi 0001
UIST4
2024 Accessible Gesture Typing on Smartphones for People with Low Vision
abstract
While gesture typing is widely adopted on touchscreen keyboards, its support for low vision users is limited. We have designed and implemented two keyboard prototypes, layout-magnified and key-magnified keyboards, to enable gesture typing for people with low vision. Both keyboards facilitate uninterrupted access to all keys while the screen magnifier is active, allowing people with low vision to input text with one continuous stroke. Furthermore, we have created a kinematics-based decoding algorithm to accommodate the typing behavior of people with low vision. This algorithm can decode the gesture input even if the gesture trace deviates from a pre-defined word template, and the starting position of the gesture is far from the starting letter of the target word. Our user study showed that the key-magnified keyboard achieved 5.28 words per minute, 27.5% faster than a conventional gesture typing keyboard with voice feedback.
Dan Zhang 0021, Zhi Li 0052, Vikas Ashok, William H. Seiple, I. V. Ramakrishnan, Xiaojun Bi 0001
UIST4
2022 Digital Technologies in Orientation and Mobility Instruction for People Who are Blind or Have Low Vision
abstract
This paper investigates the tools and practices used by Orientation and Mobility (O&M) specialists in instructing people who are blind or have low vision in concepts, skills, and techniques for safe and independent travel. Based on interviews with experienced instructors who practice in different O&M settings we find that a shortage of qualified specialists and restrictions on in-person activities during COVID-19 has accelerated interest in remote instruction and assessment, while widespread adoption of smartphones with accessibility support has driven interest in assistive apps. This presents both opportunities and challenges for a practice that is traditionally conducted in-person and assessed through qualitative observations. In response we identify multiple opportunities for HCI research in service of O&M, including: supporting a 'physician's assistant' model of remote O&M instruction and assessment, matching O&M instructors' clients with guide dogs, highlighting clients' progress towards O&M goals, and collaboratively planning routes and monitoring clients' independent travel progress.
Graham Dove, Adelle Fernando, Kim Hertz, John-Ross Rizzo, William H. Seiple, Oded Nov
Proc. ACM Hum. Comput. Interact.6