THE BRAIN CONTROLLED ROBOTIC ARM

PROBLEM STATEMENT

Our arms are huge part of our lives, we need them every time to do just about everything. Imagining our lives without arms is just a total disaster. Yet, this is how millions of people live with either one of their arm or both dysfunctional, and this is due to amputation or paralyzation which can affect all aspects of an individual's life. The most obvious is their physical life, but psychological, emotional, and social problems are also common.Even if one part is injured, the efficiency of all body functions is lost and the quality of life doesn’t remain the same.

Life becomes difficult for such individuals.

Talking about people having Arm Amputations,

Even the basic task of GRIPPING and HOLDING becomes impossible for them for rest of their lives. Its not that solutions to these problems aren’t available yet; recently ground-breaking ways to deal this problem have been successfully developed. These systems, still in development, either connect to existing neurons or to electrodes implanted into the brain to decode the signals from the brain and use them to control a robotic arm. Although they are a great way to help the disabled, these new systems are very costly to develop. The surgery required is very complicated and costly. The robotic arms that these systems use are also extremely expensive. The whole system requires hundreds of thousands to millions of dollars to develop.

SOLUTION

The idea is to work on a simpler, less costly and a feasible solution by the use of technology. A solution which is affordable to most of those who need it and might successfully enable many more people to restore not only their deformities but also their lives. The target of the solution may even be the only the basic operations like gripping, but it should pose help for the amputees.

EMOTIV EPOC+ is a 14 channel wireless EEG, designed for contextualized research and advanced brain computer interface (BCI) applications.

Easy to fit and flexible design

Wireless and rechargeable

Dense array spatial resolution ensuring ‘whole brain’ measurement

Saline based wet sensors (no sticky gels)

Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain.The helmet consists of 14 electrodes, plus two "position electrodes" and 2 gyroscopes.

The channels are, according to the international name 10-20:

AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4.

Using Mental API we are able to map the finger movements.

SOFTWARE PROCESSING

There are 4 stages through which this Brain Interfacing , BCI works :

Signal Detection

EEG Headset measures voltage fluctuations emerging from ionic current within the neurons of the brain. In the brain, there are millions of neurons, each of which creates small electric voltage fields. EEG is a superposition of many elementary signals . The main waves detected are : Alpha, Delta, Beta , Gama and Delta.

Signal Acquisition

Signal acquisition is the method of sampling signals that measure universe conditions. he data arriving at the receiver are encrypted and need to be decrypted before they can be used; the decryption takes place through emotiv application. The signals scan by Emotiv headset is distributed to Bluetooth module. The headset only detects, processes, and converts the signals into digital type. This data type is feed in to the Emotiv Software.

Signal Transmission

Bluetooth could be a wireless communication protocol. It's utilized in 2 devices for sending as well as receiving the data. Once the signals are received from the headset, from a set values of numbers and pre - processing thorough the software, signals are transmitted to the Arduino.

Mapping Signal To The Prosthetic Arm

The signal received from Emotiv Software has to be mapped to the Robotic/Prosthetic arm in the micro-controller (i.e. Arduino - Uno). The received signal will act as a command signal to control the arm. Each arm requires a servo motor for its movement. The fingers move in effect of those high torqued servo motors.

CHALLENGES AND LIMITATIONS

Emotiv Headset, due to being a non-invasive device posed problems for incorrect signal acquisitions. Decryption of the signal to the software and the henceforth pre - processing posed problems for miss interpretation.
Emotions negatively affect the experience of using the headset.
Training the headset requires lot of efforts causing great agitation and the person wearing this might get irritated causing a barrier in the hand movement.
The signal acquired from the headset used to be wrong data. Hence, Emotiv was not accurate and precise, but still vigorous practice helped us master and hack our way.
The assembling of the 3D Model Of The ForeArm and Palm took us days to properly file, attach each and every part properly with the nylon threads and with great precision so that there occurs no restriction in the movement. But due to Friction, movement of some fingers restricted, but repetition managed to remove the problem. Setting up the model was the biggest challenge of this project as it had to be an almost man-made replica of the humand hand. Hence appearance and efficiency was a challenge.
The threads due to being less elastic broke a lot of times posing the problem of re-threading and starting from the scratch.
The threads had to provide accurate torque and force to the fingers for flexion and compression.
Due to the weight of the fingers, the servo motors required a lot of power for movement. Hence, there was great power loss and this posed a huge problem.
Proper Volatage supply and regulation was essential to properly move the fingers.
Each finger was connected by nylon threads. Hence, there was a risk of breaking of the wire.
Holding And Gripping objects too were problem but were conquered successfully.
Flexion N Compression for each finger happened with certain time delay for each.
Control over the individual working of each finger explicitly was not possible due to lack of experience on software development, time limit and training status. However, it is said that the hand beautifully and efficiently performs the actions simultaneously.

OUTCOME

With the help of Emotiv Headset, and the stages of signal processing and physical form of the Hand And ForeArm, the fingers were able to show flexion and compression and the complete hand was able to hold and grip item of specific weight (i.e. not very heavy) successfully.

3D VISUALIZER FOR HAND AND FORARM

3D MODEL FOR HAND

GALLERY

FLEXION AND CONTRACTION OF THE HAND

GRIPPING FEATURE OF THE HAND

SOURCE CODE

#include<Servo.h>

Servo thumb;

Servo index;

Servo middle;

Servo ring;

Servo pinky;

char value;

int iposition = 0;

void setup() {

index.attach(3);

index.write(0);

middle.attach(5);

middle.write(0);

ring.attach(6);

ring.write(0);

pinky.attach(10);

pinky.write(0);

thumb.attach(11);

thumb.write(0);

Serial.begin(9600);

Serial.println("begin");

}

void loop() {

if(Serial.available()){

value = Serial.read();

Serial.println(value);

if(value== 'c'){

iposition = 180;

middle.write(iposition);

delay(150);

index.write(iposition);

ring.write(iposition);

pinky.write(iposition);

delay(100);

thumb.write(iposition);

}

if(value == 'f'){

iposition = 0;