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Title: | AI-ENHANCED HUMAN-MACHINE INTERFACES USING INTEGRATED MULTI-MODAL SENSING AND HAPTIC-AUGMENTED FUNCTIONS FOR DIGITAL TWIN AND METAVERSE | Authors: | SUN ZHONGDA | ORCID iD: | orcid.org/0000-0001-7365-1945 | Keywords: | Human-machine Interface; Haptic Feedback; Intelligent Robots; Wearable Sensor; Digital Twin; Metaverse | Issue Date: | 25-Jan-2023 | Citation: | SUN ZHONGDA (2023-01-25). AI-ENHANCED HUMAN-MACHINE INTERFACES USING INTEGRATED MULTI-MODAL SENSING AND HAPTIC-AUGMENTED FUNCTIONS FOR DIGITAL TWIN AND METAVERSE. ScholarBank@NUS Repository. | Abstract: | This thesis reports the development of AI-enhanced human-machine interfaces using integrated multi-modal sensing and haptic-augmented functions for digital twin and metaverse. Firstly a smart soft gripper with a fully self-powered multifunctional perception system capable of monitoring self-deformation and pressure/temperature tactile stimuli simultaneously is developed based on triboelectric and pyroelectric sensors. With machine learning analytics, high-accuracy object recognition is achieved for the developed smart robot to implement automatic item management for digital-twin-based unmanned working space. To boost the perception capability, the ultrasonic sensor is further integrated to realize the auto-positioning function and enhance the performance/robustness of the robotic identification system via multimodal data fusion technologies. Besides, wearable manipulators with multimodal sensing and haptic-feedback functions are also essential for immersive experiences in human- machine/human-robot interactions under the digital-twin-based framework when integrated with metaverse technologies. Based on this, augmented rings integrated with self-powered tactile/temperature sensors and low-power vibro-/thermo-haptic feedback units are achieved. | URI: | https://scholarbank.nus.edu.sg/handle/10635/245661 |
Appears in Collections: | Ph.D Theses (Open) |
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