Hemiplegic patients often struggle with typing rapidly and accurately using a standard keyboard. This study developed a keyboard in continuous contact with the fingers, allowing for easier and more effective typing. With the proposed keyboard, the user types with their healthy hand and paralyzed hand. The hardware and software of the finger contact keyboard were investigated. In deciding the hardware, we measured the hand shape, finger speed, and muscle fatigue using electromyography, magnetic positioning sensors, and force sensors for eight participants. Using the Pareto solution, we optimized the keyboard’s structure to maximize finger movement speed and minimize muscle fatigue. As the software of the proposed keyboard, we developed an algorithm implementing a neural network to identify intentional typing and tested the algorithm on five participants. The highest average discrimination accuracy was 99.3% when the force threshold was approximately 1.32 N. The mean accuracy achieved using the neural network was 90.8%, which is higher than that achieved using the threshold algorithm (80.4%). In 40 trials, the proposed keyboard achieved the same accuracy and speed as the standard keyboard, and the input time for a patient with hemiplegia was reduced by 16.2%.
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