International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved.
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The Smart Wheelchair Using Head Gesture Control
Prateeksha Jain1,Srishti Jangir2,Shalu Pal3, Ms. Swati Singh4
1,2, 3, Student, Department of Electronics and Communication, Meerut Institute of Technology, Meerut.
4, Assistant Professor, Department of Electronics and Communication, Meerut Institute of Technology, Meerut
Abstract—The needs of many person with disabilities can be satisfied with traditional and powered wheelchairs. About 80 percent of these disable people live in developing countries as declared by data of United Nation Development Program (UNDP). The recent developments in the robotics and artificial intelligence extend wide scope for developing an intelligent wheelchair. Many researchers are working to demonstrate these wheelchairs to help the disabled persons. This paper is to design and develop a model for a smart wheelchair using gesture control system to assist physically challenged people who have impairment in hands, legs and eyes. According to this, user can control the wheelchair with head movement. For this project we used an accelerometer to control the movement of the wheelchair. Some sensors are also used for safety. The main control unit is an AVR microcontroller Atmega2560.
Keywords—Arduino compiler,Smart Wheelchair, AVR microcontroller ATmega2560,Accelerometer, Gesture Control.
I. INTRODUCTION
A wheelchair has been an important device for biomedical sector. According to the World Facts and Statistics on Disabilities and Disability Issues, currently around 10 percent of the total world's population lives with a disability.
The world population is increasing faster and also the people with disabilities (PWD) ratio. Nowadays every person is busy in his life and there are very less people to take care about the physically challenged people. Because of this, these people find it difficult to do their daily works properly.
The wheelchair with artificial intelligence is a boon for these people. For patients who cannot use their upper and lower limbs, but are able to use skills other than above, such as the movement of the eye , face, hand , tongue or the sound of the voice, researchers have proposed different systems for wheelchairs.
The old wheelchairs require lots of effort to operate. A Smart Wheelchair using head gesture control can work as a boon for the Quadriplegics and Multiple sclerosis patients who have several disabilities.
II. TECHNIQUES USED FOR ADVANCEMENT OF WHEELCHAIR
Most recent researches are elaborated here with various methods of navigation using wheelchair. One such method is discussed in [1]. In this paper ultrasonic beacons and RF modules are attached in the ceiling of the rooms. This method has a disadvantage that RF modules and ultrasonic
beacons are fixed on the ceiling where the wheelchair is operated. Thus the system is not easily portable. Reference [2] elaborates the various design criteria to be considered while designing a navigation system for powered wheelchair.
A different method for navigation with increased autonomy has been designed using navigation sensors as in [3]. In this GPS (Global Positioning System) for positioning and navigation of the wheelchair has been used.
Some commonly used methods in these systems for an automatic wheelchair are as following:
Human Eye Controlled Navigation
In this method, the human eye is read by using the webcams to detect its movements and to control the wheelchair as in [4]. This can be designed either in the form of a wearable device or as a device attached to the wheelchair, where the user has to adjust him while sitting so that the device can detect the movement of eyes properly. Any obstacle can be detected by using another webcam which is fixed to the same structure facing away from the user towards the forward direction. One of the major disadvantages of this system is that it cannot be used by a person with squinted eyes. Another disadvantage would be that the user must continuously look into the unit and user cannot concentrate on other works. Due to which user feels uncomfortable.
The eye gaze is the factor that is used for controlling the wheelchair. When the user looks upon the system, it is recognized and sends back to the system.
Touchpad Based Navigation
This method can be a simple system where the user has to move his hand on the touch pad to move the wheelchair to the desired direction. This system, once designed will be very much compact and simple on look and hence the user will find no confusion in operating it.
Joystick Based Control Navigation and Acronyms
In this technique, a joystick is used as the primary interface between the user and the wheelchair. The user can manually control the wheelchair by using joystick. The user has to press and hold the buttons provided on the joystick to move to the desired direction. The movement is achieved by controlling the wheels using electric motors attached to the wheel according to the button pressed on the joystick. This technique makes the user more comfortable than wheelchair which uses physical power to move. So this wheelchair can be of great benefit for a paraplegic person i.e., a person with
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved.
337
disability only in hindlimbs or region lower to hip. This system can be implemented with additional feature like in [5].Non-invasive Brain Signal Interface Control (I)
In this technique, two electrodes are placed non-invasively on the scalp and signals are collected as in [6]. The detection and navigation process is done by using a P300 signal and a reference signal. P300 is an event related potential signal which is any measured brain response that has direct relation with the thought processing part of the brain. This technique has great practical application, at the same time, it is quiet risky as the user has to continuously sit and monitor the wheelchair for its navigation and can be considered as a disadvantage. The brain signals are used to select the pre-defined destination point in the menu and then the wheelchair moves in the selected direction. One of the major advantages is that no beforehand training is needed for using this system.
Non-invasive Brain Signal Interface Control Navigation (II)
According to this technique, the user faces a screen and concentrates on the area of the space to reach. A visual stimulation process elicits the neurological phenomenon and the EEG signal processing detects the target areas as in [7].
This target area represents a location that is given to the autonomous navigation system, which drives the wheelchair to the desired place while avoiding collisions with the obstacles detected by the laser scanner. This technique allows the user to navigate the wheelchair without serious training for a long term.
Touch Screen Based Navigation
Nowadays touchscreen is very much user friendly and requires very less muscle movement from the user. Touch screen is used as input device and LCD displays the user’s gesture correctly when recognized as in [8]. For obstacle detection an IR obstacle sensor unit is used in this wheelchair system.
Voice Based Control Navigation
In this method, A voice operated system for wheelchair navigation as in [5] is fixed on the wheelchair. It would be very much user friendly and comfortable for elders with limbs impairments. This method can be much benefitial to people who are unable to perform simple movements with their hands and head. This technique is language indepentent and hence can be considered universal. A voice recognition IC or module can be used, which is interfaced with a microcontroller. This IC accepts the input from the user as voice commands which are then converted to signals that a microcontroller can process. The microcontroller then produces the desired output which controls the wheelchair.
Hand Gesture Recognition Using Camera
This technique is implemented to overcome the various above discussed problem faced by the quadriplegics and paraplegics. A system is designed which uses an IR sensitive camera to identify the gesture as in [9] The captured images of the gesture are given to the microprocessor which does further processing.
III. PROPOSED HEAD GESTURE CONTROL USING ACCELEROMETER From various research papers it is studied that several techniques are used for the development of the advanced and intelligent wheelchair. They all required a computer system with a wheelchair which increases the complexity and also the maintenance of the wheelchair. To reduce this complexity an accelerometer can be used to detect the head gesture of the disabled people. This paper is proposed to design a prototype of a wheelchair controlled by head gesture using accelerometer. Here a control unit named as Arduino ATmega2560microcontroller (shown in Fig.1) is used.
Fig.1 Arduino ATmega2560
IV. TESTING OF ARDUINO ATMEGA2560 Arduino ATmega2560 is a microcontroller based development board. It is low power CMOS 8-bits RISC architecture. It requires a regulated 5V supply for microcontroller and accelerometer. Before using it for
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved.
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prototype, testing of this board is essential. So an LED test circuit is developed to test the Arduino ATmega2560. A specific coding similar to C language is done for LED test circuit using Arduino In-System Programming Platform and uploaded on the Arduino board. The LED Test circuit is connected to the Arduino board digital i/p pins and it worked according to the code.The hardware implemented for LED test circuit is shown in Fig.2.
Fig.2 LED Test Circuit
V. TESTING MODULE OF MOTOR DRIVER IC L293D After the testing of Arduino board the interfacing of the motor driver IC is done with it. It is used to drive two DC motors simultaneously, both in forward and reverse direction. The motor operations can be controlled by input logic at pins 2 & 7 and 10 & 15 through ATmega2560 microcontroller. It is tested with the motor test module (shown in Fig.3) by performing a specific program on it.
Fig.3 Testing of Motor Test Module VI. SOFTWARE IMPLEMENTATION FOR
ACCELEROMETER
An MMA7361L is a Three Axis Micro Machined Accelerometer. The g-cell is coupled with an application- specific integrated circuit (ASIC), which provides the accelerometer with amplification, signal conditioning, low- pass filter and temperature compensation. It is a low power, low profile device. It is used to recognize the head gesture of the patients. Gesture is the movement of the body part to express your thought. So the gesture of head is used to control the wheelchair. An accelerometer is an electromagnetic device that measures the change in acceleration force in three directions based on the tilt of acceleration.
Fig.4 Monitor Screen of Arduino In-System Programming Software
The Fig.4 shows the variation in the sensitivity in X,Y and Z direction when accelerometer is tilted. These values can be read by the monitor screen of the Arduino In-System Programming Software with the help of specific coding.
The ranges of X,Y and Z directions are determined for some specific movements of accelerometer. A program is prepared for the movement of wheelchair with gesture control. This software implementation is described with the help of the flow chart shown in the Fig.5.An accelerometer recognizes the variation in the sensitivity of three directions and these variations are used as the input for microcontroller device. Thus the microcontroller provides the command to motor driver IC and then it provides the movement in the wheelchair according to the coding.
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved.
339
Fig, 5 Flow Chart for Head Gesture ControlVII. HARDWARE IMPLEMENTATION The paper work is to design and develop a Smart wheelchair using head gesture. Now the prepared coding is uploading on the Arduino board and interfacing of wheels of the prototype, Motor Driver IC and accelerometer is done with the Arduino board. After that the prototype is tested whether it works according to the program. Then accelerometer is adjusted on the head on the user. The prototype worked according to the head gesture of the patient. There are following 5 types of gesture is included in this project.
Fig.5 Prototype Wheelchair
1) If the head is tilted down wheelchair moves in forward direction.
2) If the head is tilted slightly backward wheelchair moves in backward direction.
3) If the head is tilted left wheelchair rotates in left direction.
4) If the head is tilted right wheelchair rotates in right direction.
5) If the head is at stable position wheelchair stops and wait for the next command.
CONCLUSION
This paper is to design and develop a smart wheelchair using head gesture control is completed. The developed wheelchair is very user friendly and does not contain any computer system with wheelchair for controlling. So it is easy to understand and process. It only requires an accelerometer to control the movement of it. This accelerometer can be mounted on the head by using any band to recognize the head gesture.
REFERENCES
[1]H H. Seki, S. Kobayashi, Y. Kamiya, M. Hikizu, H.
Nomura, “Autonomous/Semi-autonomous navigational system of wheelchair by active ultrasonic beacons,” IEEE
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved.
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International Conference on Robotics and Automation, April 2000.[2] S. Fioretti, T. Leo, S. Longhi, “A navigational system for increasing the autonomy and the security of powered wheel chairs,”IEEE Transl. Rehabilitation Engineering, vol.
8, no. 8, December 2000.
[3]Dan Ding, BambangParmanto, Hassan A. Karimi, DuangdenRoongpiboonsopit, GedePramana, Thomas Conahan, PiyawanKasemsuppakorn, “Design considerations for a personalized wheelchair navigation system,” IEEE EMBS, CitéInternationale,Lyon, France, August 2007.
[4]Kohai Arai, Ronny Mardiyanto, “Electric wheelchair controlled by human eyes only with obstacle avoidance,”
International Journal of Research and Computer Science, December 2011.
[5]FahadWallam, Muhammad Asif, “Dynamic finger movement tracking and voice commands based smart wheelchair,” International Journal of Computer and Electrical Engineering, August 2011.
[6] I. Ituratte, J. Antelis, J. Minguez, “Synchronous EEG brain-actuated wheelchair with automated navigation,”
Kobe, Japan, May 2009.
[7] Bong-Gun Shin, Taesoo Kim, Sungho Jo, “Non- invasive brain signal interface for a wheelchair navigation,”
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International Journal of Engineering Research and Application, March-April 2012.
[9]Pei Jia ., Huosheng H. Hu.,Tao Lu., Kui Yuan., “Head gesture recognition for hands-free control of an intelligent wheelchair” , An International Journal on Industrial Robot, Vol 34 , No. 1, pp.60-68, 2007·
