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Fig 1.

Matrix of markers and information.

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Fig 2.

Schematic diagram of a visual body.

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Fig 3.

Major processing flow.

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Fig 4.

Original image and result after being augmented.

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Fig 5.

Marker recognition.

(a) Original image. (b) Binarization image. (c) Feature extraction. (d) Marker. (e) Binary image of marker. (f) Corner location. (g) Augmented results.

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Fig 6.

Marker recognition algorithm flow.

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Fig 7.

The 21 landmarks of a hand.

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Fig 8.

Operation area and screen area.

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Fig 9.

Gestures are used.

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Fig 10.

Gesture recognition accuracy.

a-j correspond to the gestures in Fig 9.

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Fig 11.

The system testing scenes.

(a) showed the moving up in the translation state. (b) showed the rotating around the X and Y axes simultaneously in the rotation state. The hand landmarks have different sizes, which represent the Z coordinate value with the wrist landmark as the zero point. The larger the diameter of the circle, the greater the absolute value of the distance. Blue means closer to the camera, and yellow means farther.

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Fig 12.

The error curves of each participants.

The horizontal axis indicates the test serial number and the vertical axis indicates the number of errors.

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Fig 13.

The average error curve.

The horizontal axis indicates the test serial number and the vertical axis indicates the average error number of all the 22 participants.

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Table 1.

The number of errors of each participants.

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Fig 14.

The time taken curves of each participants.

The horizontal axis indicates the test serial number and the vertical axis indicates the time taken in seconds.

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Fig 15.

The average time taken curve.

The horizontal axis indicates the test serial number and the vertical axis indicates the time taken of all the 22 participants in seconds.

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Table 2.

The time taken data.

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Table 3.

The questionnaire survey content and the answers.

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