Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach
Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/19/5510 |
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| author | Saba Anwer Asim Waris Hajrah Sultan Shahid Ikramullah Butt Muhammad Hamza Zafar Moaz Sarwar Imran Khan Niazi Muhammad Shafique Amit N. Pujari |
| author_facet | Saba Anwer Asim Waris Hajrah Sultan Shahid Ikramullah Butt Muhammad Hamza Zafar Moaz Sarwar Imran Khan Niazi Muhammad Shafique Amit N. Pujari |
| author_sort | Saba Anwer |
| collection | DOAJ |
| description | Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system’s performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode. |
| first_indexed | 2024-03-10T16:01:52Z |
| format | Article |
| id | doaj.art-8ad6cf30003e4968a732cb31c10becdb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:01:52Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ad6cf30003e4968a732cb31c10becdb2023-11-20T15:11:05ZengMDPI AGSensors1424-82202020-09-012019551010.3390/s20195510Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient ApproachSaba Anwer0Asim Waris1Hajrah Sultan2Shahid Ikramullah Butt3Muhammad Hamza Zafar4Moaz Sarwar5Imran Khan Niazi6Muhammad Shafique7Amit N. Pujari8School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 45200, PakistanSchool of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 45200, PakistanSchool of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 45200, PakistanSchool of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 45200, PakistanDepartment of Electrical Engineering, University of Engineering and Technology Lahore-FSD Campus, Faisalabad 38000, PakistanDepartment of Computer Sciences, Government College University, Faisalabad 38000, PakistanCenter of Chiropractic Research, New Zealand College of Chiropractic, Auckland 0600, New ZealandHead of Department, Biomedical Engineering, Riphah International University, Islamabad 45710, PakistanSchool of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UKRehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system’s performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode.https://www.mdpi.com/1424-8220/20/19/5510human machine interface (HMI)rehabilitationwheelchairquadriplegiaRaspberry Piimage gradient |
| spellingShingle | Saba Anwer Asim Waris Hajrah Sultan Shahid Ikramullah Butt Muhammad Hamza Zafar Moaz Sarwar Imran Khan Niazi Muhammad Shafique Amit N. Pujari Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach Sensors human machine interface (HMI) rehabilitation wheelchair quadriplegia Raspberry Pi image gradient |
| title | Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach |
| title_full | Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach |
| title_fullStr | Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach |
| title_full_unstemmed | Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach |
| title_short | Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach |
| title_sort | eye and voice controlled human machine interface system for wheelchairs using image gradient approach |
| topic | human machine interface (HMI) rehabilitation wheelchair quadriplegia Raspberry Pi image gradient |
| url | https://www.mdpi.com/1424-8220/20/19/5510 |
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