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Submitted by
Shameem A

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[attachment=6445]

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ABSTRACT:

This paper describes a brain controlled robotic leg which is designed to perform the normal operations of a human leg. After implanting this leg in a human, the leg can be controlled with the help of user’s brain signals alone. This leg behaves similar to a normal human leg and it can perform operation like walking, running, climbing stairs etc. The entire system is controlled with the help of advanced microcontrollers and digital signal processors. The signals are taken out from the human brain with the help of electroencephalography technique. The person can perform operations like walking, running etc just by their thought. This system will be very much suitable for those who lost their legs in accidents and the proposed system is hundred percent feasible in the real time environment with the currently available technology.
The Brain Controlled Artificial Legs are very much cost effective when compared to the normal Artificial legs which is available in the market. The reduction in cost of the proposed system is found to be above 80% when compared to the existing system. Moreover, the user can have full control over the artificial legs which is not possible in the existing system. Introduction:
A brain-computer interface (BCI), sometimes called a direct neural interface or a brainmachine interface, is a direct communication pathway between a human or animal brain and an external device. In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to the complex microprocessors and microcontrollers.
An interesting question for the development of a BCI is how to handle two learning sys - tems: The machine should learn to discriminate between different patterns of brain activity as accurate as possible and the user of the BCI should learn to perform different mental tasks in order to produce distinct brain signals. BCI research makes high demands on the system and software used. Parameter extraction, pattern recognition and classification are the main tasks to be performed in a brain signals. In this paper it is assumed that the user of this system has one leg which is functioning fully and the system is designed accordingly. This system can be extended for both the legs and it is not limited to the basic operation of human legs such as walking, running, climb - ing stairs etc. It can also perform operations like cycling, hopping etc

PRESENTED BY
KISHORE.G

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[attachment=10051]
BRAIN CONTROLLED ARTIFICIAL LEGS
INTRODUCTION
 A brain controlled robotic leg which is designed to perform the normal operations of a human leg.
 It can perform operation like walking, running, climbing stairs etc.
 Entire system is controlled with the help of advanced microcontrollers and digital signal processors.
BRAIN CONTROLLED ARTIFICAL LEGS
GENERAL BLOCK DIAGRAM
BRAIN WAVES
• Electrical activity emanating from the brain is displayed in the form of brainwaves.
• Alpha n beta waves are considered from brain.
• Converted to electrical signals by using electrode caps.
ELECTRO CAPS
SIGNAL MANAGEMENT
 Amplifier to amplify weak signals.
 Analog signal is converted to digital form using D/A convertor.
 Signal processor generates signals for µc for action.
MICRO CONTROLLER FUNCTONS
 Performs the robotic operation using stepper motor.
 Pre-programmed to do a specific operation.
 Reference signal will be already stored in the microcontroller memory in digital form.
WORKING
 Reference signal matches with the actual signal from the user’s brain.
 The robotic leg will perform the required operation.
 Usage of lithium-ion rechargeable battery for power.
NORMAL Vs BRAIN CONTROLLED LEGS
 Easy construction.
 Simple control unit.
 Less cost.
 Semi automatic.
 No need of sensors.
ADVANTAGES
 Ultra-slim design and hence they occupy very less space.
 The chance of occurrence of an error is less.
 Idea can be extended for both the legs and both the legs can be made to do operations like walk, run etc simultaneously.
CONCLUSION
 Brain Controlled Artificial Legs behaves like a normal human leg.
 With this system life can be made easier for the handicapped persons and they can also do their day-to-day activities normally without any difficulties.

presented by:
KISHORE.G

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[attachment=10086]
ABSTRACT:
This paper describes a brain controlled robotic leg which is designed to perform the normal operations of a human leg. After implanting this leg in a human, the leg can be controlled with the help of user’s brain signals alone. This leg behaves similar to a normal human leg and it can perform operation like walking, running, climbing stairs etc. The entire system is controlled with the help of advanced microcontrollers and digital signal processors. The signals are taken out from the human brain with the help of electroencephalography technique. The person can perform operations like walking, running etc just by their thought. This system will be very much suitable for those who lost their legs in accidents and the proposed system is hundred percent feasible in the real time environment with the currently available technology.
The Brain Controlled Artificial Legs are very much cost effective when compared to the normal Artificial legs which is available in the market. The reduction in cost of the proposed system is found to be above 80% when compared to the existing system. Moreover, the user can have full control over the artificial legs which is not possible in the existing system.
Introduction:
A brain-computer interface (BCI), sometimes called a direct neural interface or a brainmachine interface, is a direct communication pathway between a human or animal brain and anexternal device. In this definition, the word brain means the brain or nervous system of an organiclife form rather than the mind. Computer means any processing or computational device, fromsimple circuits to the complex microprocessors and microcontrollers.An interesting question for the development of a BCI is how to handle two learning sys -tems: The machine should learn to discriminate between different patterns of brain activity as accurateas possible and the user of the BCI should learn to perform different mental tasks in order to produce distinct brain signals. BCI research makes high demands on the system and software used. Parameter extraction, pattern recognition and classification are the main tasks to be performed in a brain signals. In this paper it is assumed that the user of this system has one leg which is functioning fully and the system is designed accordingly. This system can be extended for both the legs and it is not limited to the basic operation of human legs such as walking, running, climb - ing stairs etc. It can also perform operations like cycling, hopping etc.
Brain Waves:
Electrical activity emanating from the brain is displayed in the form of brainwaves. There are four categories of these brainwaves ranging from the most activity to the least activity. When the brain is aroused and actively engaged in mental activities, it generates beta waves. These beta waves are of relatively low amplitude, and are the fastest of the four different brainwaves. The frequency of beta waves ranges from 15 to 40 cycles a second. The next brainwave category in order of frequency is Alpha. Where beta represented arousal, alpha represents non-arousal. Alpha brainwaves are slower and higher in amplitude.
Their frequency ranges from 9 to 14 cycles per second. The next state, theta brainwaves, is typically of even greater amplitude and slower frequency. This frequency range is normally between 5and 8 cycles a second. A person who has taken time off from a task and begins to daydream is often in a theta brainwave state. The final brainwave state is delta. Here the brainwaves are of the greatest amplitude and slowest frequency. They typically center around a range of 1.5 to 4 cycles per second. They never go down to zero because that would mean that you were brain dead. But, deep dreamless sleep would take you down to the lowest frequency. Typically, 2 to 3 cycles a second.

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