Brain Controlled WheelChair

Project Title

Brain Controlled WheelChair

Project Area of Specialization Biomedical EngineeringProject Summary

Lots of people all over the world are suffering from mobility deterioration. People that have mobility issues want new technological devices to comfort themselves by convenient mobility. To help the paralyzed and disabled people, interfacing of brain with computer will help them to manage the equipments and devices without applying their muscles effort. Lots of paralyzed patient are not able to use or derive the powered wheel chairs safely. Doctors in different hospitals and fitness related institutes use Electroencephalograms (EEG) to observer and note the electrical activeness of brain. In electroencephalography process electrodes are placed on scalp and in real time electrical brain activity is monitored By keeping this brain related study in mind, an EEG based brain controlled system is planned to make an automatic wheel chair for the ease of disabled person. Because they can’t derive powered wheel chair safely due to some physical disabilities.

Improvements in technology, artificial intelligence and robotics pledge a large scope for making an developed wheelchair.BCI (Brain Computer interface) is a communication system which maintain direct route between both brain and computing equipments and devices. Our brain remains constantly active 24/7.It doesn’t matter either we are working like eating, playing, thinking & sensing or doing nothing like sleeping , sitting & taking leisure.

To read signals of brain different sensors are being used. In case of electrodes, conductive gel is use on skin in order to read signals by EEG sensor. It is convenience to use a compact EEG brainwave headset in which dry active sensor technology is used to read brain signals. To start acquiring data from brain, Gel based EEGs took almost 30 to 35 mints but in dry electrode ready to go in few minutes & take less setup time. For acquiring data from brain, we have selected EMOTIVE Insight and main reason to select INSIGHT is that it works efficiently and price is also affordable.

EMOTIVE insight has 5 electrodes (channels) with the sampling rate, 128 samples/second per channel. There is also have a 5th order sinc filter for filtering purpose which is built-in filter having frequency (0.5-43)Hz. And BLE(Bluetooth Low energy) make a connection with computer wirelessly by 2.4GHz frequency band. These properties make it better to design an efficient wheel chair.

Mainly, three main units are going to build this system Tiva C (tm4c123gh6pm) micro-controller, EMOTIVE Insight and a PC(personal computer). And main purpose is to make a less costly and easy to use, wheel chair for disabled persons.

actually we are extracting EEG signal from brain by using EMOTIV insight headset.then we train this signal in BCI software and process it. Then this signal used to run the motor of wheel chair using different logic. 

Project Objectives

The aim of the project is to help Paralyzed patients because over 80% of the SCI(Spinal cord injury ) reported to a national database in Russia occurred in males. In the United States there are approximately 250,000 persons living with SCI. In the USA, the estimated incidence of SCI is about 40 per 1 million people per year.

And SCI in Pakistan is tabulated as

Year	Incidence (per million people per year)	Percentage
2008	11	21.3%
2009	9.45	18.3%
2010	10.63	20.6%
2011	10.36	20.1%
2012	10.18	19.7

                                      SCI percentage in Pakistan

Year

2008

2009

2010

2011

2012

Project Implementation Method

RECOMMENDED SYSTEM ALGORITHM

The design of this system requires an EMOTIV insight headset placed on the head of user, which uses 5 channels EEG to sense brain actions and get signals of information. The minimal operation time is two minutes. This information was directed towards laptop to make decision by categorizing mental commands. For these training files are also created to get better EEG signals. Then these signals were processed and we trained the wheelchair based on information from EEG signals.

                

Implementation of Algorithm:

1. Emotiv Insight:

It is designed for the use of everyday life with step forward electronics that provide healthy and clean signals, by using five channels EEG. Sensors are very easy to use as they are of semi-dry polymer. This device operates wirelessly. Motion sensors of nine axes are presented in it.

                                                    

It has Driven Right Leg and Common Mode Sense references. Passive electrode DRL and active electrode is CMS.

               

1. Signal acquisition and processing:

Hardware Implementation:

 In this system signal is extracted from brain using EMOTIV insight headset having low energy Bluetooth module in it. Firstly, the headset collects data from our brain and then sends it to a computer by using EMOTIV pro software. All commands are trained on it with virtual cube’s movement. After this, signal is extracted from computer by using TM4C123GH6PM Tiva C microcontroller.

 This system was based on real time data acquisition operating system. Python based eye classification algorithm was successfully implemented both on laptop and raspberry pi 3 i.e. a low power consumption computer board providing with input and output pins with four USB ports. Rhaspberry pi supports 32 GB of memory card. RAM of raspberry pi 3 is 1 GB with ARM based controller. A small webcam mounted on a wheelchair took input from the user. After the processing, results were sent to Arduino nano. Python Bridge is a python application that is used to communicate with Arduino via pyserial function. Arduino nano, a small compact microcontroller based on ATmega 328p, took serial input from raspberry pi/laptop and provided PWM (Pulse width modulated) outputs to two IBT-2 motor driver modules. A potentiometer was connected to the analog input of Arduino to vary the speed of the left and right motor. A buck converter module was also used to step down 24V from batteries to 5V to power other components.

Benefits of the Project

A lot of people around the world endure from mobility deterioration. People with mobility deterioration required new hookup with cosmopolitan technologies to help them for cozy mobility. The aim of the project is to help Paralyzed patients because over 80% of the SCI(Spinal cord injury ) reported to a national database in Russia occurred in males. In the United States there are approximately 250,000 persons living with SCI. In the USA, the estimated incidence of SCI is about 40 per 1 million people per year. This will help those people that are fully paralyzed can not move their any part of body.

Technical Details of Final Deliverable

EMOTIV Insight Headset:
•    Number of Channels : 5 (plus CMS/DRL reference on left mastoid)
•    Sampling Method: Sequential sampling, single ADC
•    Sampling Rate: 128 SPS (2048 Hz internal)
•    EEG Resolution: 14 bits 1 LSB = 0.51?V (16 bit ADC, 2 bits instrumental noise floor discarded)
•    Filtering: Built in digital 5th order Sinc filter
•    Sensor Material: Semi dry polymer

Motors:
•    12V DC motor
•    Unloaded RPM: 55rpm at 2A current
•    Stalled current:10A

Battery:
•    Voltage:12V , 
•    Current:10A ,18 Ah
•    Led Acid battery

Drivers:
•    Max current :43A
•    Max voltage :6-27 V
•    Controlled signal:3.3-5V

TM4c123G (Microcontroller)
•    80MHz 32-bit ARM Cortex-M4-based microcontrollers CPU.
•    256KB Flash, 32KB SRAM, 2KB EEPROM.
•    Two Controller Area Network (CAN) modules.
•    USB 2.0 Host/Device/OTG + PHY.
•    Dual 12-bit 2MSPS ADCs, motion control PWMs.
•    8 UART, 6 I2C, 4 SPI.

Final Deliverable of the Project Hardware SystemCore Industry HealthOther Industries Education , Manufacturing , Others Core Technology NeuroTechOther Technologies Robotics, OthersSustainable Development Goals Good Health and Well-Being for People, Industry, Innovation and Infrastructure, Partnerships to achieve the GoalRequired Resources

Elapsed time in (days or weeks or month or quarter) since start of the project	Milestone	Deliverable
Month 1	Literature Review.	Done
Month 2	Literature Review. Signal comparison by using MATLAB and working on EEG cap	DONE
Month 3	working on EMOTIV insight headset, training of headset using EMOTIV BCI software and observation of signal on PC	DONE
Month 4	Signal Extraction using different methods: 1. HC-05 Bluetooth module 2. HM10 BLE MODULE	1.PAIRING ISSUE 2.It is not responding to MAC address Command
Month 5	3. TTL converter and also completed wheel Chair structure by connecting motor, motor derive and battery. and start writing research paper.	3. OTG cable issue Research paper in process
Month 6	For extraction purpose we have started working on EMOTIVE cloud	In progress.