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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.19174 |
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| _version_ | 1866915131288453120 |
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| author | Hoffmann, Victor Paredes-Valles, Federico Cavinato, Valentina |
| author_facet | Hoffmann, Victor Paredes-Valles, Federico Cavinato, Valentina |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Soft Materials to Controllers with NeuroTouch: A Neuromorphic Tactile Sensor for Real-Time Gesture Recognition Hoffmann, Victor Paredes-Valles, Federico Cavinato, Valentina Human-Computer Interaction 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 |
| title | From Soft Materials to Controllers with NeuroTouch: A Neuromorphic Tactile Sensor for Real-Time Gesture Recognition |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.19174 |