4.7 Article

Towards a Virtual Keyboard Scheme Based on Wearing One Motion Sensor Ring on Each Hand

Journal

IEEE SENSORS JOURNAL
Volume 21, Issue 3, Pages 3379-3387

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.3023964

Keywords

Internet of things; machine learning; virtual keyboard; wearable sensors; motion recognition

Funding

  1. National Natural Science Foundation of China [61873307]
  2. Administration of Central Funds Guiding the Local Science and Technology Development [206Z1702G]
  3. Fundamental Research Funds for the Central Universities [N2023015]
  4. Qinhuangdao Science and Technology Planning Project [201901B013]
  5. Hong Kong RGC-Joint Laboratory Funding Scheme [JLFS/E-104/18]
  6. Shenzhen Science and Technology Innovation Commission Municipality [SGDX2019081623121725]

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This paper presents an improved ring-type virtual keyboard scheme that achieves impressive performance by optimizing layout and feature selection, improving keystroke recognition accuracy and speed. This scheme has advantages in portability, small volume, and lower cost, making it a promising human-computer interface method.
In this paper, we present an improved ring-type virtual keyboard scheme that can achieve impressive performance with only one smart ring on a finger of each hand. The smart ring integrates a 6-DoF Inertial Measurement Unit (IMU) and a 3-DoF magnetometer sensor for collecting motion data during typing. First, a new keyboard layout is designed, by changing the previous rectangular layout to an arc structure, this method increases the difference in attitude angle between adjacent keys, which greatly improved the keystroke recognition accuracy. Secondly, other than the attitude angle feature, we also adopt acceleration data, gyroscope data and magnetometer data to describe the subtle differences between different keystrokes motion. Then, feature importance evaluation and feature correlation analysis were used to select features with high contribution rate and low similarity to describe keystrokes. Finally, nine effective features were selected from the attitude angle and magnetometer data for the final keystroke recognition. By weighing the number of selected features, recognition speed and recognition accuracy of training models, the keystroke recognition speed can increase by nearly 4 times while ensuring 98.53% of the keystroke recognition accuracy. This new ring-type virtual keyboard input scheme has the advantages in portability, small volume, and lower cost over many existing human-computer interface methods.

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