Abstract
With the invention of Microsoft Kinect sensor, human-computer interaction is gaining its attention and becoming available for widespread use. The previous study presented a method of Kinect-based mid-air handwritten digit recognition for Android smart phones with a recognition accuracy of only about 94.6%. In this paper, we propose an improved method based on the normalizing and scaling of path order coordinates. With that, the proposed method leads to accuracy elevation and executing time reduction. Experimental results show an average recognition accuracy rate of 96.8% was achieved for each number.
| Original language | English |
|---|---|
| Title of host publication | ICIIBMS 2015 - International Conference on Intelligent Informatics and Biomedical Sciences |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 244-249 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479985623 |
| DOIs | |
| Publication status | Published - 2016 Mar 22 |
| Event | International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2015 - Okinawa, Japan Duration: 2015 Nov 28 → 2015 Nov 30 |
Publication series
| Name | ICIIBMS 2015 - International Conference on Intelligent Informatics and Biomedical Sciences |
|---|
Other
| Other | International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2015 |
|---|---|
| Country/Territory | Japan |
| City | Okinawa |
| Period | 2015/11/28 → 2015/11/30 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Kinect sensor
- accuracy rate
- handwritten digit recognition
- human-computer interaction
- support vector machine
ASJC Scopus subject areas
- Signal Processing
- Health Informatics
- Biotechnology
- Artificial Intelligence
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