Autonomous camera based eye controlled wheelchair system using raspberry-pi

Patel, Shyam Narayan; Prakash, V.

doi:10.1109/iciiecs.2015.7192876

Cited by 31 publications

(10 citation statements)

References 8 publications

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“…In addition, safety parameters of the wheelchair's movement, such as ultrasound or IR sensors for obstacles detection, were not discussed. Another wheelchair control system has been proposed in [35], where positions of the eye pupil were tracked by employing image processing techniques using a Raspberry-Pi board and a motor drive to steer the chair to left, right, or forward directions. The open computer vision (OpenCV) library was used for image processing functions, where the HAAR cascade algorithm was used for face and eye detection, Canny edge was used for edges detection, and Hough Transform methods were used for circle detection to identify the border of the eye's pupil.…”

Section: Existing Eye-controlled Wheelchair Systemsmentioning

confidence: 99%

An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control

Dahmani

Chowdhury

Khandakar

et al. 2020

Sensors

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In the 34 developed and 156 developing countries, there are ~132 million disabled people who need a wheelchair, constituting 1.86% of the world population. Moreover, there are millions of people suffering from diseases related to motor disabilities, which cause inability to produce controlled movement in any of the limbs or even head. This paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input is images of the user’s eye that are processed to estimate the gaze direction and the wheelchair was moved accordingly. To accomplish such a feat, four user-specific methods were developed, implemented, and tested; all of which were based on a benchmark database created by the authors. The first three techniques were automatic, employ correlation, and were variants of template matching, whereas the last one uses convolutional neural networks (CNNs). Different metrics to quantitatively evaluate the performance of each algorithm in terms of accuracy and latency were computed and overall comparison is presented. CNN exhibited the best performance (i.e., 99.3% classification accuracy), and thus it was the model of choice for the gaze estimator, which commands the wheelchair motion. The system was evaluated carefully on eight subjects achieving 99% accuracy in changing illumination conditions outdoor and indoor. This required modifying a motorized wheelchair to adapt it to the predictions output by the gaze estimation algorithm. The wheelchair control can bypass any decision made by the gaze estimator and immediately halt its motion with the help of an array of proximity sensors, if the measured distance goes below a well-defined safety margin. This work not only empowers any immobile wheelchair user, but also provides low-cost tools for the organization assisting wheelchair users.

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Section: Existing Eye-controlled Wheelchair Systemsmentioning

confidence: 99%

An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control

Dahmani

Chowdhury

Khandakar

et al. 2020

Sensors

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“…Nonetheless, this proposed method only can detect 3 directions of the eyeball movements that is leftward, rightward and forward. A method for eyeball movement detection also proposed by Patel et al [14]. In this research, circle detection was used to detect the pupil locations.…”

Section: Previous Workmentioning

confidence: 99%

“…In this research, a Centroid approach is applied to determine the center of the pupil using Eq. (14).…”

Section: Leftward Forward and Rightward Movements Detectionmentioning

confidence: 99%

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Pangestu¹,

Utaminingrum²

2020

IJIES

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People with deficiency hands and feet, especially people with multiple disabilities has difficulty for navigation. Therefore, this research proposed an approach to help them using an electric wheelchair and control it using their eyeball movements. However, eyeball movement detection is still a problem due to the lack of standardized methods such as low accuracy for several gazes especially for downward and forward movements that caused by failure tracking mechanisms. Failure tracking mechanism mostly caused by failure pupil detection and tracking processes. According to the conducted research, there is a relation between eyeball movement and eyelid movement. Due to the relation, it can be concluded that the movement of the eyeball can be detected by utilize the eyelid movement without using the tracking mechanism. Hence, in this research, we propose an approach to detect the five movements of the eyeball using Eye-mark based on contour and edge detection. Our proposed method produces a brisk result with 91,2% of accuracy and better computational time compared with other marking methods. Accordingly, it is interesting on how to relate between eyeball movements and electric wheelchair navigation mechanism.

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“…This is a promising area that has been studied to improve the mobility independence of the electrical wheelchairs by enabling autonomous navigation and avoiding obstacle [1,2]. Through many research projects in this field, different solutions introduced, such as the user can control the wheelchair via touchscreens [3] and voice commands [4][5][6][7][8]. For instance, [5,6] used the android application in the mobile device and voice-recognition system.…”

Section: Introductionmentioning

confidence: 99%

Smart Autonomous Wheelchair Controlled by Voice Commands-Aided by Tracking System

2019

IJCCCE

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This study focuses on the design of an autonomous wheelchair based smart driving features for disabled persons. The movement directions and position tracking of the wheelchair are controlled and localized by pre-defined voice commands and global positioning system (GPS), respectively. Arduino microcontroller based on speaker dependent voice recognition module and tracking system based on quad-board SIM808 has been used to help the wheelchair navigation. The experimental tests of the proposed system have been done and given satisfactory results in controlling the wheelchair and making a call on demand. Hence the proposed system is a simple, easy to use and lowcost hardware for designing.

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Autonomous camera based eye controlled wheelchair system using raspberry-pi

Cited by 31 publications

References 8 publications

An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control

An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Smart Autonomous Wheelchair Controlled by Voice Commands-Aided by Tracking System

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