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- W3112564717 abstract "The number of wheelchair patients is increasing every day all over the world. Safe navigation and independent mobility are key issues for wheelchair users especially those suffering from dexterity inhibiting disorders and mobility impairments. Following modern technologies, this paper designs a control system prototype to easily navigate the wheelchair by performing finger gestures rather than operating a traditional joystick. Hand or finger gesture recognition using deep Convolutional Neural Network (CNN) and interaction with a powered wheelchair are major features of this system. This system is focused more on the flexibilities of wheelchair users by ensuring less movement of the hand as well as the cost of the implementation. To achieve these goals, some necessary steps have been performed, i.e., locating and continuously tracking the hand region, feeding the CNN model using Raspberry pi for gesture recognition, and perform navigation. After testing, our proposed system has achieved the recognition accuracy of 98.33% and a response time of 34 ms with a Raspberry Pi 4 device (1.5 GHz of CPU frequency), which is suitable for real-time wheelchair control application with reasonably low-cost." @default.
- W3112564717 created "2020-12-21" @default.
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- W3112564717 date "2020-12-17" @default.
- W3112564717 modified "2023-10-16" @default.
- W3112564717 title "Towards Developing a Real-Time Hand Gesture Controlled Wheelchair" @default.
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- W3112564717 doi "https://doi.org/10.1007/978-981-33-4673-4_28" @default.
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