TexSenseGAN: A User-Guided System for Optimizing Texture-Related Vibrotactile Feedback Using Generative Adversarial Network

• Published in: IEEE transactions on haptics Vol. 18; no. 2; pp. 325 - 339
• Main Authors: Zhang, Mingxin, Terui, Shun, Makino, Yasutoshi, Shinoda, Hiroyuki
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2025
• Subjects: Aerospace electronics; autoencoder; Deep learning; Feedback, Sensory - physiology; Generative Adversarial Networks; Generators; Haptic display; Haptic interfaces; Human in the loop; human-computer interaction; Humans; Neural Networks, Computer; Optimization; Touch; Touch Perception - physiology; Training; User-Computer Interface; Vectors; Vibration; Vibrations

Vibration rendering is essential for creating realistic tactile experiences in human-virtual object interactions, such as in video game controllers and VR devices. By dynamically adjusting vibration parameters based on user actions, these systems can convey spatial features and contribute to texture...