arXiv subjects

Roger Boldu

Publications and source records attributed to Roger Boldu.

4 recordsLinked to original sources

arXiv2026

TouchFusion: Multimodal Wristband Sensing for Ubiquitous Touch Interactions

TouchFusion is a wristband that enables touch interactions on nearby surfaces without any additional instrumentation or computer vision. TouchFusion combines surface electromyography (sEMG), bioimpedance, inertial, and optical sensing to capture multiple facets of hand activity during touch interactions. Through a combination of early and late fusion, TouchFusion enables stateful touch detection on both environmental and body surfaces, simple surface gestures, and tracking functionality for contextually adaptive interfaces as well as basic trackpad-like interactions. We validate our approach on a dataset of 100 participants, significantly exceeding the population size of typical wearable sensing studies to capture a wider variance of wrist anatomies, skin conductivities, and behavioral patterns. We show that TouchFusion can enable several common touch interaction tasks. Using TouchFusion, a wearer can summon a trackpad on any surface, control contextually adaptive interfaces based on where they tap, or use their palm as an always-available touch surface. When paired with smart glasses or augmented reality devices, TouchFusion enables a ubiquitous, contextually adaptive interaction model.

cs.HC↗

arXiv2024

picoRing: battery-free rings for subtle thumb-to-index input

Smart rings for subtle, reliable finger input offer an attractive path for ubiquitous interaction with wearable computing platforms. However, compared to ordinary rings worn for cultural or fashion reasons, smart rings are much bulkier and less comfortable, largely due to the space required for a battery, which also limits the space available for sensors. This paper presents picoRing, a flexible sensing architecture that enables a variety of \textit{battery-free} smart rings paired with a wristband. By inductively connecting a wristband-based sensitive reader coil with a ring-based fully-passive sensor coil, picoRing enables the wristband to stably detect the passive response from the ring via a weak inductive coupling. We demonstrate four different rings that support thumb-to-finger interactions like pressing, sliding, or scrolling. When users perform these interactions, the corresponding ring converts each input into a unique passive response through a network of passive switches. Combining the coil-based sensitive readout with the fully-passive ring design enables a tiny ring that weighs as little as 1.5 g and achieves a 13 cm stable readout despite finger bending, and proximity to metal.

cs.HC↗

arXiv2024

ReaWristic: Remote Touch Sensation to Fingers from a Wristband via Visually Augmented Electro-Tactile Feedback

We present a technique for providing remote tactile feedback to the thumb and index finger via a wristband device. This enables haptics for touch and pinch interactions in mixed reality (MR) while keeping the hand entirely free. We achieve this through a novel cross-modal stimulation, which we term visually augmented electro-tactile feedback. This consists of (1) electrically stimulating the nerves that innervate the targeted fingers using our wristband device and (2) concurrently, visually augmenting the targeted finger in MR to steer the perceived sensation to the desired location. In our psychophysics study, we found that our approach provides tactile perception akin to tapping and, even from the wrist, it is capable of delivering the sensation to the targeted fingers with about 50% of sensation occurring in the thumb and about 40% of sensation occurring in the index finger. These results on localizability are unprecedented compared to electro-tactile feedback alone or any prior work for creating sensations in the hand with devices worn on the wrist/arm. Moreover, unlike conventional electro-tactile techniques, our wristband dispenses with gel electrodes. Instead, it incorporates custom-made elastomer-based dry electrodes and a stimulation waveform designed for the electrodes, ensuring the practicality of the device beyond laboratory settings. Lastly, we evaluated the haptic realism of our approach in mixed reality and elicited qualitative feedback from users. Participants preferred our approach to a baseline vibrotactile wrist-worn device.

cs.HC↗

arXiv2024

RingGesture: A Ring-Based Mid-Air Gesture Typing System Powered by a Deep-Learning Word Prediction Framework

Text entry is a critical capability for any modern computing experience, with lightweight augmented reality (AR) glasses being no exception. Designed for all-day wearability, a limitation of lightweight AR glass is the restriction to the inclusion of multiple cameras for extensive field of view in hand tracking. This constraint underscores the need for an additional input device. We propose a system to address this gap: a ring-based mid-air gesture typing technique, RingGesture, utilizing electrodes to mark the start and end of gesture trajectories and inertial measurement units (IMU) sensors for hand tracking. This method offers an intuitive experience similar to raycast-based mid-air gesture typing found in VR headsets, allowing for a seamless translation of hand movements into cursor navigation. To enhance both accuracy and input speed, we propose a novel deep-learning word prediction framework, Score Fusion, comprised of three key components: a) a word-gesture decoding model, b) a spatial spelling correction model, and c) a lightweight contextual language model. In contrast, this framework fuses the scores from the three models to predict the most likely words with higher precision. We conduct comparative and longitudinal studies to demonstrate two key findings: firstly, the overall effectiveness of RingGesture, which achieves an average text entry speed of 27.3 words per minute (WPM) and a peak performance of 47.9 WPM. Secondly, we highlight the superior performance of the Score Fusion framework, which offers a 28.2% improvement in uncorrected Character Error Rate over a conventional word prediction framework, Naive Correction, leading to a 55.2% improvement in text entry speed for RingGesture. Additionally, RingGesture received a System Usability Score of 83 signifying its excellent usability.

cs.CV↗