HEAD GESTURE RECOGNITION FORHEAD GESTURE RECOGNITION FOR HANDS – FREE CONTROLHANDS – FREE CONTROL

OF AN INTELLIGENT WHEEL CHAIROF AN INTELLIGENT WHEEL CHAIRPresented by :

Suganya D (III ECE)Suganthi priya T(III ECE)

VIVEKANANDHA COLLEGE OF ENGINEERING FOR WOMEN

(Department of ECE)

OBJECTIVE 

This paper presents a novel hands-free control system for intelligent wheelchairs (IWs) based on visual recognition of head gestures for elderly and disabled people

who have restricted limb movements

 

ABSTRACT

Electric-powered wheelchairs (EPWs) have been rapidly deployed over the last 20 years

These EPWs are controlled by users’ hands and are very difficult for elderly and disabled users.

As cheap computers and sensors are embedded into EPWs, then they named as intelligent wheelchair(IWs).

INTRODUCTION

Our IWS is based on novel head gesture-based interface (HGI), namely RoboChair,

Based on the integration of the Adaboost face detection algorithm and the Camshift object tracking algorithm.

Head gesture recognition is conducted by means of real-time face detection and tracking.

SYSTEM HARDWARE STRUCTURE:

Consists of parts as follows. six ultrasonic sensors at a height of 50

cm. DSP TMS320LF2407-based controller. a Logitech 4000 Pro Webcam. a local joystick controller. Intel Pentium-M 1.6G Centrino laptop

Control system of robo chair Control system able to achieve both real

time signal processing and high performance driving control due to the following features viz.,

Excellent processing capabilities(30 MIPS)Compact peripheral integration

Two control modes of robo chair:

Intelligent control modeManual control mode

Control architecture for robo chair

Block diagram:

Manual control mode

In this mode of operation, Robochair is controlled by the JOYSTICK

JOYSTICK is connected to an A/D converter of the DSP motion controller.

Intelligent control mode

– Robochair is controlled by the proposed ( Head Gesture Interface ) HGI.

– A Logitech web camera is used to acquire the facial images of the user.

– Image data is sent to the laptop. Head gesture analysis and decision making stages are implemented.

– Finally, the laptop sends control decision to the DSP motion controlled that actuates two DC motors.

HGI ( Head Gesture Interface )

It uses two algorithm.

Adaboost face detection algorithm Camshift object tracking algorithm

ADABOOST FACE DETECTION ALGORITHM ADVANTAGES:

Extracts the Haar-like features of images that contain image frequency information.

Adaboost is able to detect profile faces High accuracy and speed in face detection

CAMSHIFT OBJECT TRACKING ALGORITHM ADVANTAGES:

Very efficient color tracking method based on image hue and achieve real time performance.

Integration of both algorithmsSince low cost IW’s have limited

onboard computing power, Adaboost face detection algorithm can’t achieve real time performance.

On the other hand, camshift face tracking algorithm runs very fast ,but is not robust to varying illumination conditions and noisy backgrounds.

So to obtain both speed and accuracy, it is necessary to integrate both algorithm.

Flowchart for integrated algorithms

Head gesture recognitionTo recognize the head gesture ,Adaboost

frontal, left profile and right profile head gesture classifiers are adopted.

If the profile face is detected, our Robochair is going to turn left or right.

By calculating the precise nose position can detect the exact frontal face head gesture using classical template matching method.

NOSE TEMPLATE MATCHING

There are five frontal head gestures to be recognized, namely:1. center frontal;2. up frontal;3. down frontal;4. left frontal; and5. right frontal.

Robochair actions for motion control commands

Rules to be followed for action for Robochair:Speed up(if frontal face up is recognized)Slow down until stop(if frontal face down is

recognized)Turn left (if left profile/frontal face is recognized)Turn right (if right profile/frontal face is

recognized )Keep speed (if central face is recognized)

DEMONSTRATION FOR PROFILE FACES

A sequence of images under head gesture control areTurn rightRight upTurn leftTurn left with

hand color noise

CONCLUSION

This paper describes the design and implementation of a novel hands-free control system for IW’s.

A robust HGI, is designed for vision-based head

gesture recognition of the Robo Chair user.

To avoid unnecessary movements caused by the user looking around randomly, our HGI is focused on the central position of the wheelchair

REFERENCES:

Bradski, G. (1998), “Real-time face and object tracking as a component of a perceptual user interface”.

Ding, D. and Cooper, R.A. (1995), “Electric poweredwheelchairs”, IEEE Control Systems,

Galindo, C., Gonzalez, J. and Fernandez-Madrigal, J.A.

(2005), “An architecture for cognitive human-robot integration. Application to rehabilitation

robotics”, Proceedings of IEEE International Conference on

Mechatronics

THANK YOU & QUERIES