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abstract

EXGbuds: Universal Wearable Assistive Device for Disabled People to Interact with the Environment Seamlessly

Published:01 March 2018Publication History

ABSTRACT

Current assistive technologies need complicated, cumbersome, and expensive equipment, which are not user-friendly, not portable, and often require extensive fine motor control. Our approach aims at solving these problems by developing, a compact, non-obtrusive and ergonomic wearable device, to measure signals associated with human physiological gestures, and thereafter generate useful commands to interact with the environment. Our innovation uses machine learning and non- invasive biosensors on top of the ears to identify eye movements and facial expressions with over 95% accuracy. Users can control different applications, such as a robot, powered wheelchair, cell phone, smart home, or other Internet of Things (IoT) devices. Combined with VR headset and hand gesture recognition devices, user can use our technology to control a camera-mounted robot (e.g., telepresence robot, drones, or any robotic manipulator) to navigate around the environment in first-person's view simply by eye movements and facial expressions. It enables a human- intuitive way of interaction totally 'touch-free'. The experimental results show satisfactory performance in different applications, which can be a powerful tool to help disabled people interact with the environment and measure other physiological signals as a universal controller and health monitoring device.

References

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  1. EXGbuds: Universal Wearable Assistive Device for Disabled People to Interact with the Environment Seamlessly

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          • Published in

            cover image ACM Conferences
            HRI '18: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
            March 2018
            431 pages
            ISBN:9781450356152
            DOI:10.1145/3173386

            Copyright © 2018 Owner/Author

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 1 March 2018

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            Acceptance Rates

            HRI '18 Paper Acceptance Rate49of206submissions,24%Overall Acceptance Rate192of519submissions,37%

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