- Open Access
- Total Downloads : 7
- Authors : S.Nandhini, Nivetha Venkatachalam, S.Sridevi
- Paper ID : IJERTCONV1IS03025
- Volume & Issue : AAMT – 2013 (Volume 1 – Issue 03)
- Published (First Online): 30-07-2018
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
Brain Controlled Artificial Legs for Physically Challenged People
S.Nandhini  Nivetha Venkatachalam S.Sridevi Associate PROFFESSOR ECE
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 users 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 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 very high 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.
A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a human or animal brain and an external device. In thisdefinition, 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 systems: 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, climbing
stairs etc. It can also perform operations like cycling, hopping etc.
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 5 and 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 seconds.
Fig 1: Different Types of Brain Waves
In the proposed system alpha waves and beta waves are used from the brain for signal processing. It is assumed that the person is in alpha state and beta state (which is the case normally) and these waves are taken out from the human brain and converted in the form electrical signals with the help of electrode caps. Figure 1 shows the different types of waves and also the mental state of the person. Those waves usually vary from a frequency of 1Hz to 40 HZ.
General Block Diagram of the System:
Fig 2: General Block Diagram of the Proposed System
Fig 2 shows the general block diagram of the proposed system. Electrode cap is placed in the scalp of the person. The signals taken out from the human brain will be in the range of mV and ÂµV. Hence they are fed to an amplifier. Then it is sent to a filter to filter out the artifacts and unwanted
signals. Then it is fed to an Analog to Digital Converter to convert the analog brain signals in to digital form. Then it is sent to a Digital Signal Processor where parameter extraction, pattern classification and pattern identification are done. Another function of the Digital Signal Processor is to control
the driving circuit. A stepper motor is used to control the positions and movements of the leg. Let us see about these blocks in detail.
Fig 3 shows a person wearing an electrode cap. These electrode caps contains electrodes which are placed onthe skull in an arrangement called 10-20 system, a placement scheme devised by the international federation of societies of EEG. In most applications 19 electrodes are placed in the scalp. Additional electrodes can be added to the standard set-up when a clinical or research application demands increased spatial resolution for a particular area of the brain. High-density arrays (typically via cap or net) can contain up to 256 electrodes more-or-less evenly spaced around the scalp. The main function ofthe electrode cap is to take the brain signals in the form of Amplifier:
The output signal from the electrode cap will be in the range of mV and ÂµV. So, these signals will not be suitable for signal processing. Hence these signals are fed to an amplifier. Each electrode is connected to one input of a differential amplifier (one amplifier per pair of electrodes); a common system reference electrode is connected to the other input of each differential amplifier. These amplifiers amplify the voltage between the active electrode and the reference (typically 1,000100,000 times, or 60100 dB of voltage gain).
The output brain signals from the amplifier will contain noise signals. These noise signals are filtered with the help of filter. A high pass and low pass filters are used for this purpose. The high-pass filter typically filters out slow artifact whereas the low-pass filter filters out high-frequency artifacts. Analog to Digital Converter:
The output signals from filter are analog in nature. . After the signal is filtered they cannot be directly fed to a digital signal processor as they are in analogform. Hence these signals are sent to an Analog to Digital converter to convert the incoming analog signals in to digital signals.
Digital Signal Processor:
Using the output signal from the A/D converter, parameter extraction, pattern classification and pattern identification are done. This is the main part of the proposed system. Once the pattern is identified it is compared with the signal stored in the memory. If any match in the signal is found then the required operation is performed.
The Dgital Signal processor performs the robotic operation with the help of a stepper motor. It will control the operations such as walking, running, etc depending upon the input signal. For different patterns of input signals it will be pre-programmed to do a specific operation. The reference signal will be already stored in the Digital Signal Processor memory. Usually an 8 bit or a 16 bit Digital Signal Processor is preferred depending upon the number of operations to be performed. The complexity of the DSP programming increases with the number of operations which has to be performed.
Working of the Proposed System:
For every human activity the brain waves changes its pattern. For example, if a person moves his/her hands then a specific pattern of brain wave is obtained and if the same person moves his/her legs then a different pattern of brain wave is obtained. Even if a person thinks of moving his/her legs a brain wave of specific pattern is produced and it is sent to the legs and then the operation of moving the legs is performed. The same brain waves are produced even for a person who is not having his/her legs. But the operation of moving the legs will not be performed due to the absence of legs. So, just by thinking of moving the legs, a brain wave which is capable of performing a specific operation is generated in the brain. Due to the lack of
electrical signals. The signals taken out from the Electrode cap are fed.
Fig 3: Placement of electrodes in 10-20 system
the appropriate system, the activity will not be performed successfully.
In the proposed system, the brain waves are pre-recorded for each operation to be performed and these waves are used as reference signals. These signals are stored in the DSP memory. For each reference signal in the DSP memory, the robotic leg is pre-programmed to do a specific operation. When the reference signal matches with the actual signal from the users brain, the robotic leg will do the pre-programmed operation with the help of the Digital Signal Processor.
For example, let us say that the user is thinking of walking. So a brain wave will beproduced. These waves are processed and then it is converted in to digital signals. These signals are compared with the pre-recorded reference signals and a match in the signal pattern will be found in the Digital Signal Processor. The operation for this particular pre-recorded signal will be pre-programmed in the Digital Signal Processor
i.e. walking and thus the Digital Signal Processor will send the control signal to the artificial robotic leg and the robotic leg will perform the required operation. Usually a stepper motor controlled robotic leg is used for this purpose. Similarly to walking, other operations can also be performed using the artificial leg. This system is very user friendly and the system can be designed according to the users requirements i.e. the number of operations required for the user can be fixed by him and the system can be designed accordingly. So the number of operations that has to be performed by the leg can be increased or decreased and the complexity of the design varies accordingly.
This idea can be extended for both the legs and both the legs can be made to do operations like walk, run etc simultaneously. Thus the system is versatile. This system is hundred percent feasible in the real time environment andit can be implanted to any human irrespective of their age.
Figure 5, 6 shows the proposed model of the leg and the internal appearance of the leg respectively.
Fig 4: Proposed Model of the artificial legs
Fig 5: Internal appearance of the artificial leg.
Fig 6: External Appearance of the artificial leg
Fig 7: Walking down the stairs using artificial
Fig 8: Artificial legs fitted to both the limbs
Fig 9: Walking with artificial legs Power Supply:
These artificial legs are powered by a small lithium-ion battery which has to be charged once in 2 day. Lithium-ion batteries have very high charge density (i.e. a light battery will store a lot of energy). They are of ultra-slim design and hence they occupy very less space. Moreover their life time will be longer when compared to other batteries. Hence they are preferred when compared to other batteries. Moreover they have longer life time when compared to other batteries.
Normal Artificial legs:
Normal Artificial Legs, available in the market, is very costly. They use a group of sensors and a complex algorithm for their operation which makes the existing system very costly. This disadvantage has been overcome in the Brain Controlled Artificial Legs as they dont use any sensors for their operation. Moreover the normal artificial legs are 100% dynamic in operation. Hence the chance of occurrence of an error is more in those systems. External appearance and output of both the legs are same. But the method of operation is different. Hence the Brain Controlled Artificial Legs are cost effective.
Difference between the Brain Controlled Artificial legs and the Normal Artificial Legs:
Brain Controlled Artificial Legs
Normal Artificial Legs
1. Ease of Construction
1. Complex in construction
2. Cost is very less.
2. Cost is about $80,000-
$90,000(Rs.35,00,000 to Rs.40,00,000)
3. User can have full control over the artificial leg.
3. User cannot have full control over the artificial leg.
4. Fully Automatic
5. Sensors are absent.
5.Sensors are present
6. Requires simple control unit.
6. Requires complex control unit.
Forty years ago, the technology was so basic. Newton said. Leg sockets were made out of wood, offering the equivalent of a door hinge at the knee. But with the recent advancement in the technology, Brain Controlled Artificial leg can be made as a reality. The performance of the proposed system will be better than the existing artificial legs as the user has full control over the Brain Controlled Artificial Legs. Hence it behaves like a normal human leg. The built-in battery lasts anywhere from 25 to 40 hours so it can support a full days activity. The recharge can be performed overnight or while traveling in a car via a cigarette lighter adapter.
The cost of the proposed system is found to be very less when compared to the existing ones. So, even the middle class people who cannot purchase the existingartificial legs can make use of this proposed system. 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.
Digital Signal Processing Principles, Algorithms and Applications by J.G.Proakis and D.G.Manolakis.
Digital Signal Processing: Principles, Devices and Applications by Norman Barrie jones and J.D.Mack Watson.
A guide to Methods in the Bio-Medical Sciences by Ronald B.Corley.
Lifescience in application of human physiology by G.J.Chuk Michael.