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arXiv · 2501.19174

From Soft Materials to Controllers with NeuroTouch: A Neuromorphic Tactile Sensor for Real-Time Gesture Recognition

Victor Hoffmann·Federico Paredes-Valles·Valentina Cavinato
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Abstract

This work presents NeuroTouch, an optical-based tactile sensor that combines a highly deformable dome-shaped soft material with an integrated neuromorphic camera, leveraging frame-based and dynamic vision for gesture detection. Our approach transforms an elastic body into a rich and nuanced interactive controller by tracking markers printed on its surface with event-based methods and harnessing their trajectories through RANSAC-based techniques. To benchmark our framework, we have created a 25 min gesture dataset, which we make publicly available to foster research in this area. Achieving over 91% accuracy in gesture classification, a 3.41 mm finger localization distance error, and a 0.96 mm gesture intensity error, our real-time, lightweight, and low-latency pipeline holds promise for applications in video games, augmented/virtual reality, and accessible devices. This research lays the groundwork for advancements in gesture detection for vision-based soft-material input technologies. Dataset: Coming Soon, Video: Coming Soon

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Victor Hoffmann, Federico Paredes-Valles, Valentina Cavinato. 2025-01-31. From Soft Materials to Controllers with NeuroTouch: A Neuromorphic Tactile Sensor for Real-Time Gesture Recognition. https://arxiv.org/abs/2501.19174

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