BRAIN CONTROLLED CAR FOR DISABLED USING ARTIFICIAL INTELLIGENCE

Similar documents
BRAIN CONTROLLED CAR FOR DISABLED USING ARTIFICIAL INTELLIGENCE

A SEMINAR REPORT ON BRAIN CONTROLLED CAR USING ARTIFICIAL INTELLIGENCE

BRAIN CONTROLLED CAR FOR DISABLED USING ARTIFICIAL INTELLIGENCE

Motor Imagery based Brain Computer Interface (BCI) using Artificial Neural Network Classifiers

Presented by: V.Lakshana Regd. No.: Information Technology CET, Bhubaneswar

Non-Invasive Brain-Actuated Control of a Mobile Robot

1. INTRODUCTION: 2. EOG: system, handicapped people, wheelchair.

Analysis of brain waves according to their frequency

The Application of Human-Computer Interaction Idea in Computer Aided Industrial Design

Linear Motion Servo Plants: IP01 or IP02. Linear Experiment #0: Integration with WinCon. IP01 and IP02. Student Handout

Off-line EEG analysis of BCI experiments with MATLAB V1.07a. Copyright g.tec medical engineering GmbH

BRAIN COMPUTER INTERFACE (BCI) RESEARCH CENTER AT SRM UNIVERSITY

Robot Task-Level Programming Language and Simulation

Smart Phone Accelerometer Sensor Based Wireless Robot for Physically Disabled People

I.1 Smart Machines. Unit Overview:

Dipartimento di Elettronica Informazione e Bioingegneria Robotics

ME7752: Mechanics and Control of Robots Lecture 1

Session 11 Introduction to Robotics and Programming mbot. >_ {Code4Loop}; Roochir Purani

from signals to sources asa-lab turnkey solution for ERP research

A.I in Automotive? Why and When.

An Introduction to Programming using the NXT Robot:

Programming PIC Microchips

A Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures

BRAINWAVE RECOGNITION

AutoBench 1.1. software benchmark data book.

Android Phone Based Assistant System for Handicapped/Disabled/Aged People

Chapter 1. Robots and Programs

SIMULATION MODELING WITH ARTIFICIAL REALITY TECHNOLOGY (SMART): AN INTEGRATION OF VIRTUAL REALITY AND SIMULATION MODELING

The Virtual Reality Brain-Computer Interface System for Ubiquitous Home Control

Non Invasive Brain Computer Interface for Movement Control

Evaluation of Connected Vehicle Technology for Concept Proposal Using V2X Testbed

AN AUTONOMOUS SIMULATION BASED SYSTEM FOR ROBOTIC SERVICES IN PARTIALLY KNOWN ENVIRONMENTS

MAGNATEST ECM * Low cost eddy current module for non destructive testing using the magnetoinductive

Modelling and Simulation of Tactile Sensing System of Fingers for Intelligent Robotic Manipulation Control

Design of intelligent vehicle control system based on machine visual

Image 1, Ref - see slide WHAT IS A ROBOT? A look at characteristics of robots using the LEGO EV3 as a specific example (50 minutes)

Advanced Robotics Introduction

I. INTRODUCTION MAIN BLOCKS OF ROBOT

Classifying the Brain's Motor Activity via Deep Learning

This list supersedes the one published in the November 2002 issue of CR.

RP5-GM32 Radio Replacement & Steering Wheel Control Interface with OnStar Retention for General Motors Vehicles

Overview of Challenges in the Development of Autonomous Mobile Robots. August 23, 2011

A Brain-Computer Interface Based on Steady State Visual Evoked Potentials for Controlling a Robot

Mobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands

RED TACTON ABSTRACT:

Team Autono-Mo. Jacobia. Department of Computer Science and Engineering The University of Texas at Arlington

Azaad Kumar Bahadur 1, Nishant Tripathi 2

Key-Words: - Neural Networks, Cerebellum, Cerebellar Model Articulation Controller (CMAC), Auto-pilot

INTELLIGENT GUIDANCE IN A VIRTUAL UNIVERSITY

Mechatronics Educational Robots Robko PHOENIX

MASTER SHIFU. STUDENT NAME: Vikramadityan. M ROBOT NAME: Master Shifu COURSE NAME: Intelligent Machine Design Lab

Thanks to Autocheck function, it is possible to perform a complete check-up of the robot thanks to a stepby-step

Teleoperation and System Health Monitoring Mo-Yuen Chow, Ph.D.

MAGNATEST ECM * * Low-cost eddy-current module for non-destructive testing using the magneto-inductive method

Relationship to theory: This activity involves the motion of bodies under constant velocity.

ANIMA: Non-conventional Brain-Computer Interfaces in Robot Control through Electroencephalography and Electrooculography, ARP Module

RED TACTON.

Nebraska 4-H Robotics and GPS/GIS and SPIRIT Robotics Projects

MULTI-LAYERED HYBRID ARCHITECTURE TO SOLVE COMPLEX TASKS OF AN AUTONOMOUS MOBILE ROBOT

Lab 1: Testing and Measurement on the r-one

How Small Can Robots Be?

understanding sensors

GPS Tracking System Using Car Charger

Brain Machine Interface for Wrist Movement Using Robotic Arm

Human Authentication from Brain EEG Signals using Machine Learning

Human-to-Human Interface

Test Booklet. Subject: LA, Grade: 04 LEAP Grade 4 Language Arts Student name:

Pre-Day Questionnaire

acknowledgments...xv introduction...xvii 1 LEGO MINDSTORMS NXT 2.0: people, pieces, and potential getting started with the NXT 2.0 set...

Multi-Robot Teamwork Cooperative Multi-Robot Systems

LDOR: Laser Directed Object Retrieving Robot. Final Report

RP5-GM31 Radio Replacement & Steering Wheel Control Interface with OnStar Retention for General Motors Vehicles

The Design of Intelligent Wheelchair Based on MSP430

The Datasheet and Interfacing EE3376

Fabrication of the kinect remote-controlled cars and planning of the motion interaction courses

M.Sinduja,S.Ranjitha. Department of Electrical & Electronics Engineering, Bharathiyar Institute of Engineering For Women, Deviyakurichi.

RECOMMENDATION ITU-R BS

BRAINWAVE CONTROLLED WHEEL CHAIR USING EYE BLINKS

145M Final Exam Solutions page 1 May 11, 2010 S. Derenzo R/2. Vref. Address encoder logic. Exclusive OR. Digital output (8 bits) V 1 2 R/2

[Kumar, 5(12): December2018] ISSN DOI /zenodo Impact Factor

Design of WSN for Environmental Monitoring Using IoT Application

An Autonomous Self- Propelled Robot Designed for Obstacle Avoidance and Fire Fighting

Agent. Pengju Ren. Institute of Artificial Intelligence and Robotics

Your EdVenture into Robotics 10 Lesson plans

Bio-signal research. Julita de la Vega Arias. ACHI January 30 - February 4, Valencia, Spain

The Study of Methodologies for Identifying the Drowsiness in Smart Traffic System: A Survey Mariya 1 Mrs. Sumana K R 2

Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface

ARTIFICIAL INTELLIGENCE - ROBOTICS

Aztec Micro-grid Power System

Vision Ques t. Vision Quest. Use the Vision Sensor to drive your robot in Vision Quest!

HeroX - Untethered VR Training in Sync'ed Physical Spaces

The Man-Machine-Man(M 3 ) Interfacing With the Blue Brain Technology

The Perception. Is Reality. Test Bench

Putting It All Together: Computer Architecture and the Digital Camera

F8101ALE User s Guide

DREAM BIG ROBOT CHALLENGE. DESIGN CHALLENGE Program a humanoid robot to successfully navigate an obstacle course.

Hands-free Operation of a Small Mobile Robot*

Experiment: P34 Resonance Modes 1 Resonance Modes of a Stretched String (Power Amplifier, Voltage Sensor)

Laboratory of Advanced Simulations

Intelligent Bus Tracking and Implementation in FPGA

Transcription:

BRAIN CONTROLLED CAR FOR DISABLED USING ARTIFICIAL INTELLIGENCE 1. ABSTRACT This paper considers the development of a brain driven car, which would be of great help to the physically disabled people. Since these cars will rely only on what the individual is thinking they will hence not require any physical movement on the part of the individual. The car integrates signals from a variety of sensors like video, weather monitor, anti-collision etc. it also has an automatic navigation system in case of emergency. The car works on the asynchronous mechanism of artificial intelligence. It s a great advance of technology which will make the disabled, abled. In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton and the Ratio Club in England.

interface, is a direct communication pathway between a human or animal brain (or brain cell culture) and an external device. In one-way BCIs, computers either accept commands from the brain or send signals to it (for example, to restore vision) but not both. Two-way BCIs would allow brains and external devices to exchange information in both directions but have yet to be successfully implanted Most researchers hope that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them. A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project. in animals or humans. 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 silicon chips (including hypothetical future technologies such as quantum computing) 2.INTRODUCTION The video and thermogram analyzer continuously monitor activities outside the car. A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine Once the driver (disabled) nears the car. The security system of the car is activated. Images as well as thermo graphic results of the driver are previously fed into the database of the computer. If the video images match with the database entries then the security system advances to the next stage. Here the thermo graphic image verification is done with the database. Once the driver passes this stage the door slides to the sides and a ramp is lowered from its floor. The ramp has flip actuators in its lower end. Once the driver enters the ramp, the flip actuates the ramp to be lifted horizontally. Then robotic arms assist the driver to his seat. As soon as the driver is seated the EEG (electroencephalogram) helmet, attached to the top of the seat, is lowered and suitably placed on the driver s head. A wide

screen of the computer is placed at an angle aesthetically suitable to the driver. Each program can be controlled either directly by a mouse or by a shortcut. For starting the car, the start button is clicked. Accordingly the computer switches ON the circuit from the battery to the A.C.Series Induction motors. (EEG) measured from the human scalp. We refer to this initial design as the Low- Frequency Asynchronous Switch Design (LF-ASD) (Fig.1). 3.BIOCONTROL SYSTEM The biocontrol system integrates signals from various other systems and compares them with originals in the database. It comprises of the following systems: Brain-computer interface Automatic security system Automatic navigation system Now let us discuss each system in detail. 3.1.BRAIN COMPUTER INTERFACE Brain-computer interfaces will increase acceptance by offering customized, intelligent help and training, especially for the non-expert user. Development of such a flexible interface paradigm raises several challenges in the areas of machine perception and automatic explanation. The teams doing research in this field have developed a single-position, brain-controlled switch that responds to specific patterns detected in spatiotemporal electroencephalograms Fig.1 LF-ASD The EEG is then filtered and run through a fast Fourier transform before being displayed as a three dimensional graphic. The data can then be piped into MIDI compatible music programs. Furthermore, MIDI can be adjusted to control other external processes, such as robotics. The experimental control system is configured for the particular task being used in the evaluation. Real Time Workshop generates all the control programs from Simulink models and C/C+ + using MS Visual C++ 6.0. Analysis of data is mostly done within Mat lab environment. FEATURES OF EEG BAND Remote analysis data can be sent and analyzed in real-time over a network or modem connection. Data can be fully exported in raw data, FFT & average formats. Ultra low noise balanced DC coupling

amplifier. Max input 100microV p-p, minimum digital resolution is 100 microv p-p / 256 = 0.390625 micro V p-p. FFT point can select from 128 (0.9375 Hz), 256 (0.46875 Hz), 512 (0.234375 Hz resolution). Full Brain wave driven sound control, support for 16 bit sound; user configurable Full image capture and playback control; user configurable. Support for additional serial ports via plug-in boar; allows extensive serial input & output control. Infinite real-time data acquisition (dependent upon hard drive size). Real-time 3-D & 2-D FFT with peak indicator, Raw Data, and Horizontal Bar displays with Quick Draw mode. Fig. 2: EEG Transmission Full 24 bit color support; data can be analyzed with any standard or user. Customized color palettes; color cycling available in 8 bit mode with QuickDrawmode. Interactive real-time FFT filtering with Quick Draw mode. Real-time 3-D FFT (left, right, coherence and relative coherence), raw wave, sphere frequency and six brain wave switch in one OpenGL display. Full Brainwave driven Quick Time Movie, Quick Time MIDI control; user configurable Fig. 3 EEG 3.1.1.TEST RESULTS COMPARING DRIVER ACCURACY WITH/WITHOUT BCI

1. Able-bodied subjects using imaginary movements could attain equal or better control accuracies than able-bodied subjects using real movements. 2. Subjects demonstrated activation accuracies in the range of 70-82% with false activations below 2%. 3. Accuracies using actual finger movements were observed in the range 36-83% 4. The average classification accuracy of imaginary movements was over 99% of the driver. Fig.5 Eyeball Tracking Fig.4 Brain-to- Machine Mechanism As the eye moves, the cursor on the screen also moves and is also brightened when the driver concentrates on one particular point in his environment. The sensors, which are placed at the front and rear ends of the car, send a live feedback of the environment to the computer. The steering wheel is turned through a specific angle by electromechanical actuators. The angle of turn is calibrated from the distance moved by the dot on the screen. The principle behind the whole mechanism is that the impulse of the human brain can be tracked and even decoded. The Low-Frequency Asynchronous Switch Design traces the motor neurons in the brain. When the driver attempts for a physical movement, he/she sends an impulse to the motor neuron. These motor neurons carry the signal to the physical components such as hands or legs. Hence we decode the message at the motor neuron to obtain maximum accuracy. By observing the sensory neurons we can monitor the eye movement Fig.6 Electromechanical Control Unit

computer drives the car automatically. Video and anti-collision sensors mainly assist this drive by providing continuous live feed of the environment up to 180 m, which is sufficient for the purpose. Fig.7 Sensors and Their Range 3.2.AUTOMATIC SECURITY SYSTEM The EEG of the driver is monitored continually. When it drops less than 4 Hz then the driver is in an unstable state. A message is given to the driver for confirmation and waits for sometime, to continue the drive. A confirmed reply activates the program for automatic drive. If the driver is doesn t give reply then the computer prompts the driver for the destination before the drive. 3.3.AUTOMATIC NAVIGATION SYSTEM Fig.8 EEG Analysis Window 4.CONCLUSION When the above requirements are satisfied and if this car becomes cost effective then we shall witness a revolutionary change in the society where the demarcation between the abler and the disabled vanishes. Thus the integration of bioelectronics with automotive systems is essential to develop efficient and futuristic vehicles, which shall be witnessed soon helping the disabled in every manner in the field of transportation. As the computer is based on artificial intelligence it automatically monitors every route the car travels and stores it in its map database for future use. The map database is analyzed and the shortest route to the destination is chosen. With traffic monitoring system provided by xm satellite radio the 5.REFERENCE 1. 'Off-line Classification of EEG from the "New York Brain- Computer Interface (BCI)" Flotzinger, D., Kalcher, J., Wolpaw, J.R., McFarland, J.J., and Pfurtscheller, G., Report #378, IIG-Report Series, IIG: Institutes for Information Processing, Graz University of

Technology, Austria 1993. 2. "Man-Machine Communications through Brain-Wave Processing" Keirn, Z.A. and Aunon, J.I., IEEE Engineering in Medicine and Biology Magazine, March 1990. 3.Automotive engineering, SAE, June 2005 4.Automotive mechanics, Crouse, tenth edition, 1993 5. "The brain response interface: communication through visually-induced electrical brain responses" Sutter, E.E., Journal of Microcomputer Applications, 1992, 15: 31-45.