Volume 22, Issue 4 (April 2022)                   Modares Mechanical Engineering 2022, 22(4): 281-290 | Back to browse issues page


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1- Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Tehran, Iran
2- Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Tehran, Iran , samavati@pardisiau.ac.ir
Abstract:   (1556 Views)
In this paper, a simple and low weight gadget, without the need for a nurse, is designed in a low cost, taking into safety, flexibility and mobility for the user. This paper presents a new system that the controller system is designed and implemented based on the combination of acceleration sensor data and image processing system with the aim of controlling the movement of the smart wheelchair. This design, the user can take control of the smart wheelchair without using hands. This gadget can be a good solution for disabled people to make their life easy.
The results of the smart wheelchair control testing with hybrid controller indicate that the accuracy and speed of the wheelchair response high. The experimental tests suggest that the proposed design of controller gadget is efficient and low cost as well as allowing disabled people to more easily control their wheelchairs and to lead independent lives.
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Article Type: Original Research | Subject: Mechatronics
Received: 2021/08/3 | Accepted: 2021/12/5 | Published: 2022/03/30

References
1. Klaib AF, Alsrehin NO, Melhem WY, Bashtawi HO, Magableh AA. Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies. Expert Systems with Applications. 2020 Sep 28:114037. [DOI:10.1016/j.eswa.2020.114037]
2. Dahmani M, Chowdhury ME, Khandakar A, Rahman T, Al-Jayyousi K, Hefny A, Kiranyaz S. An intelligent and low-cost eye-tracking system for motorized wheelchair control, Sensors, 2020 Jan;20(14):3936. [DOI:10.3390/s20143936]
3. Antoniou E, Bozios P, Christou V, Tzimourta KD, Kalafatakis K, G Tsipouras M, Giannakeas N, Tzallas AT. EEG-Based Eye Movement Recognition Using the Brain-Computer Interface and Random Forests. Sensors. 2021 Jan;21(7):2339. [DOI:10.3390/s21072339]
4. Renuka K, Harini R, Balaji V, Ashok N. Raspberry Pi based Multi-optional Wireless Wheelchair Control and Gesture Recognized Home Assist System. InIOP Conference Series: Materials Science and Engineering 2021 Mar 1 (Vol. 1084, No. 1, p. 012071). IOP Publishing. [DOI:10.1088/1757-899X/1084/1/012071]
5. Poornima G. Information Fusion Based Wheelchair Control for Paralyzed Patient. In2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) 2020 Oct 7 (pp. 921-927). IEEE.
6. Wang H, Yan F, Xu T, Yin H, Chen P, Yue H, Chen C, Zhang H, Xu L, He Y, Bezerianos A. Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, From Single Modal to Hybrid Modal, From Synchronous to Asynchronous. IEEE Access. 2021 Apr 7;9:55920-38. [DOI:10.1109/ACCESS.2021.3071599]
7. Bonci A, Fiori S, Higashi H, Tanaka T, Verdini F. An Introductory Tutorial on Brain-Computer Interfaces and Their Applications. Electronics. 2021 Jan;10(5):560. [DOI:10.3390/electronics10050560]
8. Fadheel BA, Mahdi AJ, Jaafar HF, Nazir MS, Obaid MS, Musa SH. Speed control of a wheelchair prototype driven by a DC Motor through real EEG brain signals. InIOP Conference Series: Materials Science and Engineering 2020 (Vol. 671, No. 1, p. 012036). IOP Publishing. [DOI:10.1088/1757-899X/671/1/012036]
9. Alhakeem ZM, Ali RS, Abd-Alhameed RA. Wheelchair Free Hands Navigation Using Robust DWT_AR Features Extraction Method With Muscle Brain Signals. IEEE Access. 2020 Mar 31;8:64266-77. [DOI:10.1109/ACCESS.2020.2984538]
10. Gómez-Carrasquilla C, Quirós-Espinoza K, Carrasquilla-Batista A. Wheelchair control through eye blinking and IoT platform. In2020 IEEE 11th Latin American Symposium on Circuits & Systems (LASCAS) 2020 Feb 25 (pp. 1-4). IEEE. [DOI:10.1109/LASCAS45839.2020.9068989]
11. Rakasena, E. P. G., & Herdiman, L. (2020, February). Electric wheelchair with forward-reverse control using electromyography (EMG) control of arm muscle. In Journal of Physics: Conference Series (Vol. 1450, No. 1, p. 012118). IOP Publishing. [DOI:10.1088/1742-6596/1450/1/012118]
12. Cheraghpour, Farzad, et al. "FARAT1: an Upper Body Exoskeleton Robot." 2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM). IEEE, 2017. [DOI:10.1109/ICRoM.2017.8466183]
13. Kuntal K, Banerjee I, Lakshmi PP. Design of Wheelchair based on Electrooculography. In2020 International Conference on Communication and Signal Processing (ICCSP) 2020 Jul 28 (pp. 0632-0636). IEEE. [DOI:10.1109/ICCSP48568.2020.9182157]
14. Sidik MM, Ghani SC, Saniman MN. A REAL-TIME EMG PATTERN RECOGNITION CONTROL METHOD FOR ACTIVATION OF INSTRUMENTED WHEELCHAIR POWER ASSIST SYSTEM. PalArch's Journal of Archaeology of Egypt/Egyptology. 2020 Nov 3;17(9):3430-41.
15. Tang W, Wang A, Ramkumar S, Nair RK. Signal identification system for developing rehabilitative device using deep learning algorithms. Artificial intelligence in medicine. 2020 Jan 1;102:101755. [DOI:10.1016/j.artmed.2019.101755]
16. Ghorbel A, Amor NB, Jallouli M. A survey on different human-machine interactions used for controlling an electric wheelchair. Procedia Computer Science. 2019 Jan 1;159:398-407. [DOI:10.1016/j.procs.2019.09.194]
17. Madona P, Nisa HK, Wijaya YP, Akhyan A. The Design Of Wheelchair Systems With Raspberry Pi 3-Based Joystick Analog And Voice Control. InIOP Conference Series: Materials Science and Engineering 2020 May 1 (Vol. 846, No. 1, p. 012032). IOP Publishing. [DOI:10.1088/1757-899X/846/1/012032]
18. Sharifuddin MS, Nordin S, Ali AM. Comparison of CNNs and SVM for voice control wheelchair. IAES International Journal of Artificial Intelligence. 2020 Sep 1;9(3):387. [DOI:10.11591/ijai.v9.i3.pp387-393]
19. Rabhi Y, Mrabet M, Fnaiech F. A facial expression controlled wheelchair for people with disabilities. Computer methods and programs in biomedicine. 2018 Oct 1;165:89-105. [DOI:10.1016/j.cmpb.2018.08.013]
20. Arijilli L, Reddy S. Control of Wheelchair by Eye Movement Using Image Processing.
21. Sowmya M, MR MU. Eye Gaze Controlled Wheelchair.
22. Singh R, Rani H, Khan JH, Komal K. Eyeball Controlled Wheelchair. Science and Technology. 2020 Jul;5(04).
23. Dhyavanpalli RS, Chinchole MG, Bansode RS. SMART WHEELCHAIR FOR PHYSICALLY IMPAIRED.
24. Dey P, Hasan MM, Mostofa S, Rana AI. Smart wheelchair integrating head gesture navigation. In2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) 2019 Jan 10 (pp. 329-334). IEEE. [DOI:10.1109/ICREST.2019.8644322]
25. Hasan S, Faisal F, Sabrin S, Tong Z, Hasan M, Debnath D, Hossain MS, Siddique AH, Alam J. A Simplified Approach to Develop Low Cost Semi-Automated Prototype of a Wheelchair. InUniversity of Science and Technology Annual (USTA) 2020.
26. Viola P, Jones M. Robust real-time object detection. International journal of computer vision. 2001 Jul 13;4(34-47):4.

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