University of Hertfordshire

From the same journal

By the same authors

Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach

Research output: Contribution to journalArticlepeer-review

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Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach. / Anwer, Saba; Waris, Asim; Sultan, Hajrah; Butt, Shahid Ikramullah; Zafar, Muhammad Hamza; Sarwar, Moaz; Niazi, Imran Khan; Shafique, Muhammad; Pujari, Amit N.

In: Sensors (Switzerland), Vol. 20, No. 19, 5510, 26.09.2020, p. 1-13.

Research output: Contribution to journalArticlepeer-review

Harvard

Anwer, S, Waris, A, Sultan, H, Butt, SI, Zafar, MH, Sarwar, M, Niazi, IK, Shafique, M & Pujari, AN 2020, 'Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach', Sensors (Switzerland), vol. 20, no. 19, 5510, pp. 1-13. https://doi.org/10.3390/s20195510

APA

Anwer, S., Waris, A., Sultan, H., Butt, S. I., Zafar, M. H., Sarwar, M., Niazi, I. K., Shafique, M., & Pujari, A. N. (2020). Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach. Sensors (Switzerland), 20(19), 1-13. [5510]. https://doi.org/10.3390/s20195510

Vancouver

Author

Anwer, Saba ; Waris, Asim ; Sultan, Hajrah ; Butt, Shahid Ikramullah ; Zafar, Muhammad Hamza ; Sarwar, Moaz ; Niazi, Imran Khan ; Shafique, Muhammad ; Pujari, Amit N. / Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach. In: Sensors (Switzerland). 2020 ; Vol. 20, No. 19. pp. 1-13.

Bibtex

@article{91cbf7459bbe4ecd9d62382de2d777b9,
title = "Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach",
abstract = "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{\textquoteright}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.",
keywords = "AMR voice, Human machine interface (HMI), Image gradient, Image processing, Open-CV, Quadriplegia, Raspberry Pi, Rehabilitation, Wheelchair",
author = "Saba Anwer and Asim Waris and Hajrah Sultan and Butt, {Shahid Ikramullah} and Zafar, {Muhammad Hamza} and Moaz Sarwar and Niazi, {Imran Khan} and Muhammad Shafique and Pujari, {Amit N.}",
note = "{\textcopyright} 2020 The Author(s). This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.",
year = "2020",
month = sep,
day = "26",
doi = "10.3390/s20195510",
language = "English",
volume = "20",
pages = "1--13",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "19",

}

RIS

TY - JOUR

T1 - Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach

AU - Anwer, Saba

AU - Waris, Asim

AU - Sultan, Hajrah

AU - Butt, Shahid Ikramullah

AU - Zafar, Muhammad Hamza

AU - Sarwar, Moaz

AU - Niazi, Imran Khan

AU - Shafique, Muhammad

AU - Pujari, Amit N.

N1 - © 2020 The Author(s). This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PY - 2020/9/26

Y1 - 2020/9/26

N2 - 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.

AB - 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.

KW - AMR voice

KW - Human machine interface (HMI)

KW - Image gradient

KW - Image processing

KW - Open-CV

KW - Quadriplegia

KW - Raspberry Pi

KW - Rehabilitation

KW - Wheelchair

UR - http://www.scopus.com/inward/record.url?scp=85091722750&partnerID=8YFLogxK

U2 - 10.3390/s20195510

DO - 10.3390/s20195510

M3 - Article

C2 - 32993047

AN - SCOPUS:85091722750

VL - 20

SP - 1

EP - 13

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 19

M1 - 5510

ER -