Brain Controlled Wheelchair with Smart Feature
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909449668526080 |
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| author | Sarkar, Noyon Kumar Roy, Moumita Maniruzzaman, Md. |
| author_facet | Sarkar, Noyon Kumar Roy, Moumita Maniruzzaman, Md. |
| contents | In Asia, many individuals with disabilities rely on wheelchairs for mobility. However, some people, such as those who are fully disabled or paralyzed, cannot use traditional wheelchairs despite having fully functioning cognitive abilities. To address this issue, we propose the development of an electric wheelchair that can be controlled using EEG signals and eye blinks. The project utilizes a MindWave Mobile device and Arduino to enable seamless control. Additionally, various sensors are incorporated to enhance the system's reliability. An ultrasonic sensor helps avoid unexpected collisions, while a smoke sensor detects hazardous smoke levels, triggering an automatic alert via a short message to a designated person. Similarly, if the passenger falls from the wheelchair, a notification will also be sent. The wheelchair's movement is controlled via an Android application, with eye-blink detection serving as the primary input method for navigation. This innovative design offers a cost-effective solution, making it accessible for widespread use. By integrating these advanced features, the system can be implemented on motorized wheelchairs to better support individuals with disabilities and enhance their independence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03371 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Brain Controlled Wheelchair with Smart Feature Sarkar, Noyon Kumar Roy, Moumita Maniruzzaman, Md. Human-Computer Interaction In Asia, many individuals with disabilities rely on wheelchairs for mobility. However, some people, such as those who are fully disabled or paralyzed, cannot use traditional wheelchairs despite having fully functioning cognitive abilities. To address this issue, we propose the development of an electric wheelchair that can be controlled using EEG signals and eye blinks. The project utilizes a MindWave Mobile device and Arduino to enable seamless control. Additionally, various sensors are incorporated to enhance the system's reliability. An ultrasonic sensor helps avoid unexpected collisions, while a smoke sensor detects hazardous smoke levels, triggering an automatic alert via a short message to a designated person. Similarly, if the passenger falls from the wheelchair, a notification will also be sent. The wheelchair's movement is controlled via an Android application, with eye-blink detection serving as the primary input method for navigation. This innovative design offers a cost-effective solution, making it accessible for widespread use. By integrating these advanced features, the system can be implemented on motorized wheelchairs to better support individuals with disabilities and enhance their independence. |
| title | Brain Controlled Wheelchair with Smart Feature |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.03371 |