Yan Ma

schoolStony Brook Universitybar_chart#1216 Global Rank

Total Papers

HCI Rank

#1216

Research Areas

Years Active

Recent Publications

KeySense: LLM-Powered Hands-Down, Ten-Finger Typing on Commodity Touchscreens

Existing touchscreen software keyboards prevent users from resting their hands, forcing slow and fatiguing index-finger tapping (“chicken typing”) instead of familiar hands-down ten-finger typing. We present KeySense, a purely software solution that preserves physical keyboard motor skills. KeySense isolates intention…

UIST '252nd AuthorVoice Accessibility +2

Enabling Auto-Correction on Soft Braille Keyboard

A soft Braille keyboard is a graphical representation of the Braille writing system on smartphones. It provides an essential text input method for visually impaired individuals, but accuracy and efficiency remain significant challenges. We present an intelligent Braille keyboard with auto-correction ability, which use…

CHI '255th+ AuthorFoot & Wrist Interaction +2

SpellRing: Recognizing Continuous Fingerspelling in American Sign Language using a Ring

Fingerspelling is a critical part of American Sign Language (ASL) recognition and has become an accessible optional text entry method for Deaf and Hard of Hearing (DHH) individuals. In this paper, we introduce SpellRing, a single smart ring worn on the thumb that recognizes words continuously fingerspelled in ASL. Spe…

CHI '251st AuthorEV Charging & Eco-Driving Interfaces +1

LLM Powered Text Entry Decoding and Flexible Typing on Smartphones

Large language models (LLMs) have shown exceptional performance in various language-related tasks. However, their application in keyboard decoding, which involves converting input signals (e.g. taps and gestures) into text, remains underexplored. This paper presents a fine-tuned FLAN-T5 model for decoding. It achieves…

UIST '232nd AuthorForce Feedback & Pseudo-Haptic Weight +1

TouchType-GAN: Modeling Touch Typing with Generative Adversarial Network

Models that can generate touch typing tasks are important to the development of touch typing keyboards. We propose TouchType- GAN, a Conditional Generative Adversarial Network that can sim- ulate locations and time stamps of touch points in touch typing. TouchType-GAN takes arbitrary text as input to generate real…

CHI '233rd AuthorHonorable MentionHand Gesture Recognition +2

WordGesture-GAN: Modeling Word-Gesture Movement with Generative Adversarial Network

Word-gesture production models that can synthesize word-gestures are critical to the training and evaluation of word-gesture keyboard decoders. We propose WordGesture-GAN, a conditional generative adversarial network that takes arbitrary text as input to generate realistic word-gesture movements in both spatial (i.e.,…

CHI '225th+ AuthorHand Gesture Recognition +1

Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens

Suggesting multiple target candidates based on touch input is a possible option for high-accuracy target selection on small touchscreen devices. But it can become overwhelming if suggestions are triggered too often. To address this, we propose SATS, a Suggestion-based Accurate Target Selection method, where target sel…

UIST '211st AuthorHand Gesture Recognition +1

Modeling Touch Point Distribution with Rotational Dual Gaussian Model

Touch point distribution models are important tools for designing touchscreen interfaces. In this paper, we investigate how the finger movement direction affects the touch point distribution, and how to account for it in modeling. We propose the Rotational Dual Gaussian model, a refinement and generalization of the Du…

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