| Autor: |
Ghosh, Ahona, Saha, Sriparna, Ghosh, Lidia |
| Zdroj: |
International Journal of Information Technology; August 2023, Vol. 15 Issue: 6 p3025-3036, 12p |
| Abstrakt: |
Stroke and many other neural disorders affecting motor functions lead to a significant degradation in the quality of life. The solution to the problem can be found in integrating brain commands with a mind driven wheelchair system for the disabled persons leading to motor rehabilitation, for achieving efficient control in different environments. But, the performance issues like the lack of robustness and reliability of the existing prototypes motivated us to design a multimodal rehabilitation framework using brain-computer interfacing for people with movement complications with the help of an electroencephalogram and a mobile robot. The current framework utilizes Fuzzy Vector Quantization as a motor imagery-based movement classifier after channel selection from electroencephalographic signals and feature extraction from that. The proposed model of robot navigation based on movement classification achieves 96.45% accuracy and it outperforms existing works by a remarkable margin. After getting successful results, a mobile robot-based prototype of a wheelchair (here, using TurtleBot3) is designed and presented with real-time implementation proving its suitability in day-to-day rehabilitative platforms. |
| Databáze: |
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