seminar topics

CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011

COPO306-1

Real-time Hand Gesture Recognition Techniques for

Human-Computer Interaction

ANSHUL SHARMA

Department of Electronics and Communication Engineering, MNNIT Allahabad

Email ID: [email protected]

Abstract- This paper presents some real-time

video processing techniques for intelligent and

efficient hand gesture recognition, with an aim

of establishing a virtual interfacing platform for

Human-Computer Interaction (HCI). The first

step of the process is colour segmentation based

skin detection, followed by area-based noise

filtering. If a gesture is detected, the next step is

to calculate a number of independent

parameters of the available visual data and

assign a distinct range of values of each

parameter to a predefined set of different

gestures. The final step is the hierarchical

mapping of the obtained parameter values to

recognise a particular gesture from the whole set.

Deliberately, the mapping of gestures is not

exhaustive, so as to prevent incorrect mapping

(misinterpretation) of any random gesture not

belonging to the predefined set. The applications

of the same are inclusive of, but not limited to,

Sign Language Recognition, robotics, computer

gaming etc. Also, the concept may be extended,

using the same parameters, to facial expression

recognition techniques.

1. INTRODUCTION

Gestures and gesture recognition are terms

increasingly encountered in discussions of human-

computer interaction. The term includes character

recognition, the recognition of proof readers

symbols, shorthand, etc. Every physical action

involves a gesture of some sort in order to be

articulated. Furthermore, the nature of that gesture

is generally an important component in establishing

the quality of feel to the action. The general

problem is quite challenging due a number of issues

including the complicated nature of static and

dynamic hand gestures, complex backgrounds, and

occlusions. Attacking the problem in its generality

requires elaborate algorithms requiring intensive

computer resources. Due to real-time operational

requirements, we are interested in a

computationally efficient algorithm.

Previous approaches to the hand gesture

recognition techniques include the use of markers

on various points on the hand, including fingertips.

Calculation and observation of relative placement

and orientation of these markers specifies a

particular gesture. The inconvenience of placing

markers on the users hand makes this an infeasible

approach in practice. Another approach is to use

sensor- fitted gloves to detect the orientation and

other geometrical properties of the hand. The

demerit of this approach is its cost ineffectiveness.

The approach proposed in this text is quite user-

friendly as it does not require any kind of markers

or special gloves for its operation. Also, the

memory requirements are low because the

subsequent video frames are not stored in memory,

they are just processed and overwritten. Obviously,

it adds a new challenge to make the algorithm very

fast and efficient, fulfilment of which is ensured by

using low-complexity calculation techniques. For

the ease of implementation, the proposed algorithm

is based on three basic assumptions:

1. The background should be dark. 2. The hand should always be at a constant

distance from the camera.

3. There should be a time gap of at least 200 ms between every two gestures.


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2. DATA EXTRACTION

The concept of video processing is based upon

data extraction from subsequent frames of the video.

The video is accessed frame by frame and the

following actions are performed on every

subsequent frame: (Some frames may be skipped,

without loss of information, because at an average

frame rate of 30 fps, any two subsequent frames

will have almost the same data.)

2.1 Hand Block Detection:

The first step of the gesture recognition process is

to detect if there is a gesture at all. It is done by skin

colour detection and area based detection of the

hand block. Also, the gesture should only be taken

into consideration if the whole hand is captured in

the video frame. This is achieved by calculating the

geometrical centroid of the hand block and

imposing a range restriction on the coordinates of

the centroid.

2.2 Elliptical Approximation:

The concept of elliptical approximation is the

heart of the gesture recognition process discussed in

this text. Once the hand block is detected, i.e. the

presence of a gesture (valid or invalid) is ensured, it

is approximated by an ellipse having the same

second-moments as the hand block detected.

Having obtained such a mathematical modelling,

we can interpret a number of independent

parameters from the geometrical properties of this

ellipse. This concept is depicted in Figure 1.

Figure 1: Elliptical approximation of hand block

The Orientation of the hand object is simply the

inclination of the major axis of the ellipse thus

obtained, from the positive horizontal axis.

Orientation is measured in a range of -90 to +90

degrees, as depicted in Figure 2.

Figure 2: Orientation of the hand block

The Eccentricity of the ellipse is simply the ratio

of semi-major axis (the distance between the two

foci) to the major axis. The value of eccentricity of

an ellipse lies between 0 and 1, both inclusive. An

ellipse with zero eccentricity is actually a circle.

The other extremity (eccentricity = 1) represents a

straight line. Thus, the eccentricity is a measure of

the narrowness of the ellipse. Higher be the

eccentricity, narrower is the ellipse (and vice versa).

2.3 Counting the number of fingers:

A very fast approach to count the number of

fingers in a gesture is discussed in this text, using

the data extracted by the means of elliptical

approximation as discussed already. With the vertex

of the ellipse as centre and a radius equal to 0.35

times the length of major axis, a circle is drawn.

The number of times the periphery of this circle

intersects any part of the hand block gives a direct

measure of the number of fingers in the gesture.

Figure 3 explains the concept. Clearly, if the circle

intersects the hand block at N places, then (N-1) is

the number of fingers in the gesture. As far as the

selection of the radius is concerned, 0.35 times the

major axis length was experimentally found to be


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the most optimum. A larger scaling factor (greater

than 0.4) entirely covers the smaller fingers and

hence, the presence of thumb and the little finger in

a gesture cant be detected. On the other hand, a

small scaling factor (less than 0.3) is unable to

distinguish between two fingers separated by a

small gap, because the gap between the fingers is

lesser near the palm.

Figure 3: Counting the number of fingers

2.3 Perimeter Estimation:

Another distinguishing characteristic of a gesture

is the length of the periphery of the hand object.

Perimeter is calculated by simply counting the

number of pixels on the boundary of the hand

object.

Figure 4(a)

Figure 4(b)

Figure 4: Gestures having equal Areas but unequal Perimeters

Its significance can be appreciated by

acknowledging the fact that two gestures with same

area can have different perimeters. The hand

objects in Figure 4(a) and Figure 4(b) cover

approximately the same area, but the perimeter of

the hand object in Figure 4(a) is larger than that of

the hand object in Figure 4(b).

3. GESTURE RECOGNITION PROCESS

Having calculated the various parameters of the

hand block by mathematical modelling using

elliptical approximation, we now assign a unique

set of values of all these parameters to every gesture,

to be able to distinguish each one uniquely. Instead

of calculating all the parameters for every gesture, it

is efficient to categorize the gestures into different

hierarchical levels, so that only relevant properties

of every gesture are calculated, to make the

algorithm time-efficient. Statistically speaking, the

most optimum order is achieved by adopting the

following hierarchical recognition process:

1. Eccentricity. The first step is to mathematically

model the gesture by elliptical approximation and

calculate the eccentricity of the ellipse thus

obtained. If the eccentricity is too low (less than

0.5), it shows that the ellipse is wide enough

(approaching a circular shape), as is the case with

the gestures shown in Figure 5.


COPO306-4

(a)

(b)

Figure 5: Gestures with Eccentricity < 0.5

On the other hand, if the eccentricity is too high

(greater than 0.95), it shows that the ellipse is very

narrow (almost a straight line), which is the case

with the gestures shown in Figure 6. In both these

cases, there is no need to count the number of

fingers, as it is obviously zero.

2. Number of Fingers. If the value of Eccentricity

lies between 0.5 and 0.95 (both exclusive), the

algorithm to calculate the number of fingers is

applied to the gesture frame.

3. Perimeter Estimation. Perimeter estimation is

necessary as a distinguishing parameter only in a

few cases, when the number of fingers is less than

three. This is obvious from Figure 4, which shows

one case in which perimeter estimation is required

as a distinguishing feature. Another similar case is

complimentary to these gestures, with negative

Orientation.

Figure 6: Gestures with Eccentricity > 0.95

4. Orientation. Orientation calculation is necessary

for every gesture, irrespective of the values of the

other parameters associated with it. This is because

the proposed gesture set is so made that every

gesture has a complimentary gesture whose all the

properties are the same as the former, with the only

difference being that its Orientation is the negative

of that of former. Since Orientation calculation is a

must for every gesture, this step may be executed at

any level, without any effect on the complexity of

the algorithm. Keeping it at the end of the process is

just one of the several equally efficient options.

4. THE ENTIRE GESTURE SET

The algorithm and the parameters already

discussed altogether can uniquely recognise a

gesture out of a closed set of 32 distinct gestures.

The complete gesture set is shown in Figure 7.

5. APPLICATIONS

Real-time gesture recognition is an emerging and

challenging field of Computer Vision. The main

applications of the same include, but are not limited

to:


COPO306-5

Figure 7: The Entire Gesture Set

Human- Computer Interaction (HCI).

Wireless robotic control mechanism.

Virtual Keyboard Interfacing Platform.

Security systems.

6. SCOPE FOR FUTURE WORK

The proposed gesture recognition techniques can

prove to be an integral basis for research work in

certain emerging fields, viz. Computer Vision, Real

Time Signal Processing, HCI, etc. Some major

fields that provide a challenging scope for the

future work of this text are discussed below:

Motion Gestures: So far we have discussed only the recognition of static gestures. The above

mentioned techniques, coupled with a concept

of motion recognition, can be used for

recognition of motion gestures.

No Background Limitation: The above mentioned techniques are based on the

assumption that the background is always dark.

However, using dynamic colour segmentation

schemes, the same techniques can be applied

without this limitation also.

Double-Handed Gestures: All the same concepts can be easily extended to gestures

involving both the hands at once. Doing so will

increase the size of the gesture set by about 5

times (with a substantial increase in complexity,

though).

Three- dimensional Gestures: The proposed techniques deal with the recognition of only

two-dimensional gestures (hence the

assumption that the hand should always be at

constant distance from the camera, as discussed

earlier). However, if we use an additional

camera, orthogonal to the first one, we can

obtain the information about the distance of the

hand from the camera. This can be helpful for

efficient recognition of three-dimensional

gestures.

Facial Expression Recognition: The concept of mathematical modelling of gestures using

Elliptical Approximation can be quite efficient

for the recognition of facial expressions.

Artificial Intelligence: Real-time Gesture Recognition, coupled with a concept of Neural

Networks, may prove to be an integral

milestone in the field of Artificial Intelligence.

7. REFERENCES

[1]. Juan P. Wachs, Helman Stern, Yael Edan: Cluster

Labeling and Parameter Estimation for the Automated Setup

of a Hand-Gesture Recognition System, IEEE Transactions

on Systems, Man and CyberneticsPart A: Systems and

Humans, Vol. 35, No. 6, November 2005.

[2]. Hyung-Ji Lee, Jae-Ho Chung: Hand Gesture Recognition

Using Orientation Histogram, 1999 IEEE Tencon.

[3].Rafael C. Gonzalez, Richard Eugene Woods: Digital

Image Processing, Third Edition, Prentice Hall Publications,

2008.


COS0102-1

Modelling Neuron for Biomedical Applications: A Review

1Taslima Ahmed , 2 Dr Jiten Ch Dutta

1Dept. of Electronics and Instrumentation Engg. IIMT College of Engineering, Greater Noida, Knowledge Park-III, UP-201306, India

2Dept of ECE, Tezpur University , Napaam Post, Tezpur, Assam 784 028, India

[email protected]

Abstract: Modelling of neuron including the action of synapse has played an important role in the

field of biomedical engineering and neurology for simulation of receptor function and electrical

activity of the postsynaptic neuron. In this paper, we review some literatures concerning the

development of different neuron models giving special emphasis on Dutta and Roy models.

Keywords Neuron, Synapse, MOSFET, Postsynaptic membrane.

Over past few years, many electronic circuits

have been developed to reproduce the behaviour of

nerve axons [1]-[5]. A very good account of this

type of modelling is reviewed by Harmon et al [6]

and Lewis [7]. But among these models,

neuroscientists have, so far, utilized Hodgkin-

Huxley (H-H) model as a circuit analog of the

axonal membrane. In this model, the capacitance of

the lipid bilayer of postsynaptic membrane is

represented by CM and is found to be constant and

the membrane resistance is determined in terms of

three parallel conductances gNa, gk, and g0 as shown

in Fig.1. The conductances gNa, gK, and go represent

the membrane permeability of Sodium, Potassium

and other ions respectively. ENa, and EK are

respectively the chemical potentials of Sodium and

Potassium i.e., Nernstian membrane potential for

Sodium and Potassium. EO is the resting potential.

The gk and gNa conductances are found to be time

and voltage dependent.

Fig.1: H-H model

The total current in this model is given by:

I = Im+Io-INa+IK (1)

If Vm be the postsynaptic membrane potential

established by the ionic and capacitive membrane

current then

I = C(dVm/dt)+gO(VmEO)gNa(VmENa)+gK

(VmEK) (2)

The equations (1) and (2) are called H-H equations

which are simple and capable of explaining the

activity of neuron with the help of variable

permeability of membrane for different ions, e.g.,

sodium, potassium and other ions. But this model

has not explained the function of synapses on which

the variable permeability of postsynaptic membrane

arises.Refering to the biological activities of neuron,

the primary mode of communication between two

neurons is a biochemical process that occurs at

synapse. Synapse is essentially a junction called

synaptic cleft between two neurons namely

presynaptic and postsynaptic neurons. Signal from

presynaptic neuron to postsynaptic neuron is

transmitted through neurotransmitters released by

presynaptic neuron terminals in to the synaptic cleft.

Neurotransmitters diffuse through the cleft and then

bind with the specific receptor sites of the

membrane of postsynaptic neuron. This binding

mechanism initiates the opening of transmitter


COS0102-2

gated ion channels resulting in to flow of ions into

the cell or out of the post synaptic cell.

The membrane of post synaptic neuron has two

types of ion channels excitatory and inhibitory.

The excitatory channels are those which are specific

to sodium ions and inhibitory channels are those

which are specific to Chloride ions. The flow of

Sodium ions into the cell causes a membrane

potential called excitatory postsynaptic membrane

potential (EPSP) whereas the flow of Chloride ions

causes an inhibitory postsynaptic membrane

potential (IPSP).The electrical mechanism of

synapse is shown in Fig 2. When an action potential

from the presynaptic neuron arrives at its terminals

connecting the cleft, neurotransmitters are released

into the cleft which diffuse through the cleft and

bind with the receptor sites of the postsynaptic

membrane. This binding mechanism opens the ion

channels situated at the membrane surface and ions

move into or out of the membrane. If the synapse is

excitatory, Sodium ions flow into the cell resulting

into positive current. As a result the membrane

depolarizes. If sufficient number of Sodium

channels open, then membrane potential will be

greater than the threshold VT of the neuron and

initiates an action potential. If the synapse is

inhibitory, Chloride ions move into the cell,

resulting into negative current. As a result the

membrane hyperpolarizes. If the numbers of

opening of Chloride channels are sufficiently large

then membrane potential will be able to initiate an

action potential in negative direction. The

presynaptic equivalent circuit is shown in Fig 3(a),

where I is the total current from ionic channels of

all synapses and E1 ,E2, ., EM represent the

chemical potentials of each corresponding ions. For

example, EM may be ENa or may be ECl. The total

current I will stimulate the postsynaptic neuron to

initiate an action potential [8]. Fig 3(b) shows the

equivalent circuit of a synapse which is developed

by adding H-H equivalent circuit with the

presynaptic circuit shown in Fig 3(a) [9].The

postsynaptic membrane consists of a lipid bilayer

and transmembrane protein ion channels. Some ion

channels such as sodium, chloride etc. are

controlled by the neurotransmitters that bind with

the receptor sites, i.e. the

Fig.2: Electrical mechanism of synapse

Fig.3(a): Equivalent circuit of a presynaptic neuron

Fig. 3(b): Electrical equivalent circuit of synapse

amount of ionic current is dependent upon the

activity of the transmitter-receptor binding. In

simplest case, the binding reaction may be

represented as


COS0102-3

Neuro-transmitter+Receptor(Closed)1

2

K

K

Neuro-

transmitter Receptor(Open) (3)

Where K1 and K2 are the forward and backward

rate constants respectively. The transmitter gated

channels, therefore, have variable conductance

dependence on the binding activity of transmitters.

Dutta and Roy therefore have modeled transmitter

gated ion channels by MOSFET, because MOSFET

functions as a voltage controlled conductance in its

linear region [10]. In this model they have

considered gate voltage as a time dependent voltage

given by

Vg(t)=V0[(1exp(-k1t)+exp(-k2t)U(t-tm)]

(4)

Where K1 and K2 are time constants analogous to

the rate constants of equation (3), U(t-tm) is the

Heaviside function andVo is a voltage proportional

to the maximum attainable conductance, when all

the transmitter-gated channels for a specific ion are

open. Based on this, they have developed a

biologically motivated model as shown in Fig.4 (a).

Their circuit models both for excitatory and

inhibitory synapses are shown in Fig.4(b) and 4(c)

respectively. In both these models they have

divided the postsynaptic membrane into three

patches to represent spatial summation of the

sodium current and chlorine current controlled by

respectively sodium and chloride conductances [10].

Fig.4(a): Biologically motivated model of

postsynaptic membrane.

Fig.4(b): Circuit model for excitatory action of

synapse

Fig.4(c): Circuit model for inhibitory action of

synapse

The simulation results from this model [Fig.5]

indicate that this model can be used in neuro

bioengineering area for simulation of

neurotransmitter-receptor binding activity and

electrical activity of the postsynaptic neuron.


COS0102-4

Fig.5: Simulation results of excitatory and

inhibitory actions of postsynaptic Membrane. Top

waveform represents the EPSP and bottom

waveform represents the IPSP.

In an another model, they have used ion sensitive

field effect transistor (ISFET) as circuit analog and

incorporated into the famous Hodgkin-Huxley (H-H)

model of neuron to substitute the variable Na+ and

Cl- conductances, the details of which may be

obtained in reference [11]. They have mentioned

that such model has additional advantages over

MOSFET based model. The advantages that they

have high lighted are: (i) Measurement of different ions that diffuse through the post synaptic membrane and hence pH (ii) measurement of neurotransmitters diffused through the synaptic cleft by converting the ISFET into neurotransmitter sensitive enzyme modified FET (ENFET). This model, according to them, may become a useful research unit in neurology for biotelemetry applications. The second advantage that ENFET can also be used as circuit analog, in an effort, they have modeled it using ENFET sensitive to acetylcholine neurotransmitter for simulation of acetylcholine gated ion channels of the post synaptic membrane at the synaptic cleft[12].

MOSFET and ISFET based electrical models both

for excitatory and inhibitory actions of neurons

have been reviewed. It is concluded that ISFET

based models are more biologically motivated as

these are compatible with biological medium and

there is possibility of measurement of

neurotransmitters diffused through the synaptic

cleft by converting the ISFET into neurotransmitter

sensitive ENFET. These biologically motivated

models may become useful research and teaching

units in biomedical area in general and neurology in

particular.

REFERENCES [1] Hodgkin, A, L and Huxley, A. F., A

quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiol, 117. 500-544(1952)

[2] Hodgkin, A. L., Ionic movements and electrical activity in giant nerve fibers, Proceedings of the Royal Society of London. Series B, Biological Sciences, Vol. 148, 1-38(1957)

[3] Fitzhugh, R., Threshold and plateaus in the Hodgkin-Huxley nerve equations, J. Gen. Physiology, 43, 867-(1960)

[4] Johnson and Hanna, Membrane model: A single transistor analog of excitable membrane, J. Theoret. Bio, 22, 401-411(1969)

[5] E.R. Lewis, Neuroelectric potentials derived from an extended version of the Hodgkin and Huxley model, J. Theor. Biol. Vol.10,125-158, 1965

[6] L.D. Harmon and E.R.Lewis, Neural modelling, Physiol. Rev. , Vol, 48, 513-591, 1966

[7] E.R.Lewis, Using electronic circuits to model simple neuroelectric interactions, Proc.IEEE,vol 56, 931-949, June 1968.

[8 ] Xiao-lin Zhang, A Mathematical Model of a Neuron with Synapses

based on Physiology, Nature Proceedings,

npre.2008.1703.1. March

2008.

[9] Soumik Roy, Jiten Ch Dutta, Shikhamoni

Phukan, Integrate-and-

Fire based Circuit model for simulation of

excitatory and inhibitory

synapses, Canadian Journal on Biomedical

Engineering & Technology

Vol. 1, No. 2 March 2010, 49-51. [10] Jiten Ch Dutta and Soumik Roy

Biologically motivated Circuit model for simulation of excitatory and inhibitory synapses, Canadian Journal on Biomedical Engineering & Technology Vol. 1, No. 2 June 2010, 49-51.

[11] Jiten Ch Dutta and Soumik Roy, An Electronic Circuit Model for simulation of Synaptic Communication: The NEUROISFET for Wireless Biotelemetry, accepted for publication in the IEEE conf. on wireless communication, 24-25 Feb, 2011, BITS, MESRA

[12] Jiten Ch Dutta and Soumik Roy Biologically inspired Circuit model for simulation of Acetylcholine gated ion channels of the Postsynaptic membrane at synaptic cleft,

CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27

2011

SIP0109-1

Electricity Generation by People Walk through

Piezoelectric Shoe: An Analysis

1.Dr. Monika Jain,

2.Ms. Usha Tiwari,

3.Mohit Gupta,

4.Magandeep singh Bedi

1.Member IEEE, IETE, Professor-Dept of Electronics & Instrumentation Engg,

Galgotias College of Engineering & Technology,Greater Noida,UP, INDIA 2.

Assistant Professor-Dept of Electronics & Instrumentation

Galgotias College of Engineering & Technology,Greater Noida,UP, INDIA .3&4.

B.Tech, 4th

year student Dept of Electronics & Instrumentation

Galgotias College of Engineering & Technology,Greater Noida,UP, INDIA [email protected]

[email protected].

[email protected]

[email protected]

Abstract In todays, high crisis of

electrical power, there has been an

increasing demand for low-power and

portable-energy sources due to the

development and mass consumption of

portable electronic devices.

Furthermore, the portable-energy

sources must be associated with

competitive market price,

environmental issues and other imposed

regulations. These tremendous demands

support lots of research in the area of

portable-energy generation methods. In

this scope, piezoelectric materials has

always been chosen as an attractive

choice for energy generation and

storage. In this paper, different

techniques are being explored and

analysed to generate electricity by usage

of piezoelectric crystal. In-depth study

and analysis to describes the use of

piezoelectric polymers in order to utilize

and the best optimisation of energy

from people-walk and the fabrication of

a smart shoe, capable of generating and

accumulating the energy has peen

presented.

Keywords Energy harvesting, PZT,

uninterrupted power supplies.

I. INTRODUCTION

Piezoelectric generators are based on

piezoelectric effect i.e. the ability of

certain materials to create electrical

potential when responding to mechanical

changes. In real time application, when

compressed or expanded or otherwise

changing shape a piezoelectric material

will output certain voltage. This effect is

also possible in reverse in the sense that

putting a charge through the material will

result in it changing shape or undergoing

some mechanical stress. These materials

are useful in a variety of ways. Certain

piezoelectric materials can handle high

voltage extremely well and are useful in

transformers and other electrical

components. Piezoelectric crystals are

boon of sensor technology field as it might

be possible to make motors, reduce

vibrations in sensitive environments, used

as an energy collector and in many more

applications. In todays power crisis world,

one of the most interesting area is energy

collection and generation. In this paper, a

cheap and smart however a reliable

mechanism to generate energy capable

enough to charge our phone, MP3 players

has been explored and analysed. An

interesting methodology of power


2011

SIP0109-2

generation through the walking steps of

human being is reviewed and presented

here. The sole of shoe could be constructed

of piezoelectric materials and every step a

person took would begin to generate

electricity. This smart mechanism of

generation of electricity through shoe sole

could then be stored in a battery or used

immediately in personal electronics

devices.

II. LITERATURE REVIEW

The most common methodology of

shoe power generators include dielectric

elastomers [1] and piezoelectric ceramics

[2,3]. The elastomer demonstrated

significant power output but it required a

large bias (2 kV) and the heavy

construction is likely to negatively affect

the user experience. The power harvesting

shoe reported in [2] and [3] uses

piezoelectric ceramic bi-morphs for power

harvesting. As piezoelectric materials were

employed, no bias voltage was needed.

However, a complex PZT/metal bi-morph

was required and the power output after

dc/dc conversion and regulation was low

(


2011

SIP0109-3

There are two types of piezoelectric

signals that can be used for technological

applications: the direct piezoelectric effect

that describes the ability of a given

material to transform mechanical strain

into electrical signals and the converse

effect, which is the ability to convert an

applied electrical solicitation into

mechanical energy. The direct

piezoelectric effect is more suitable for

sensor applications, whereas the converse

piezoelectric effect is most of the times

required for actuator applications[12].

High-performance films, prepared by

researchers [14-15] is also explored. In this

the electromechanical properties of the

film were improved by a treatment that

consists of pressing, stretching, and poling

at a high temperature [14].

III. CONCLUSION

In this paper, an analysis for Electricity-

Genration for low power devices is done.

Different methodologies for generation of

electricity is reviewed and presented. We

analysed that some of the methodologies

are not feasible due to too much circuitry

in real time portable charging and some

are feasible but they are on an analysis

stage. We have found that piezoelectric

generators implanted in shoe can provide

a great achievement if collaboratively an

effort is made to bring a commercial

battery charger for low power house

devices, just by utilization of walking steps

of a person.

REFERENCES

[1] Roy Kornbluh, Power from plastic:

how electroactive polymer artificial

muscles will improve portable ower

generation in the 21st century military,

Presentation [Online],

Available:http://www.dtic.mil/ndia/2003tri

service/korn.ppt

[2] John Kymisis, et.al., Parasitic power

harvesting in shoes in Proc. of the 2nd

IEEE Int. Conf. On Wearable Computing,

Pittsburgh. PA, pp. 132-139, 19-20 Oct.

1998.

[3] S. Shenck and J. Paradiso, Energy

scavenging with shoe-mounted

piezoelectrics, IEEE Micro, Vol. 21, pp.

30-42, May-June, 2001.

[4] P. Miao, et.al., Micro-Machined

Variable Capacitors for Power

Generation, in Proc. Electrostatics

Edinburgh, UK, 23-27 Mar. 2003.

[5] Mitcheson, P.D.; Green, T.C.;

Yeatman, E.M.; Holmes, A.S.,

"Architectures for vibration-driven

micropower generators," Journal of

Microelectromechanical Systems, vol.13,

no.3, pp. 429-440, June 2004.

[6] Ville Kaajakari, Practical MEMS,

Small Gear Publishing, 2009.

[7] M.Duffy & D.Carroll,

Electromagnetic generators for power

harvesting 35th AM^ IEEE Power

Electronics Specialists Conference

Aachen, Germany, 2004 ;pp. 2075-2081

[8]M. El-hami, P. Glynne-Jones, M.

White, M. Hill, S. Beeby, E. James, D.

Brown, and N. Ross, Design and

fabrication of a new vibration-based

electromechanical power generator,

Sens. Actuators A, Phys., vol. 92, no. 13,

pp. 335342, Aug. 2001.


2011

SIP0109-4

[9] M. Miyazaki, H. Tanaka, G. Ono, T.

Nagano, N. Ohkubo, T. Kawahara, and K.

Yano, Electric-energy generation using

variablecapacitive resonator for power-

free LSI, in Proc. ISLPED, 2003, pp.

193198.

[10] C. Keawboonchuay and T. G. Engel,

Maximum power generation in a

piezoelectric pulse generator, IEEE

Trans. Plasma Sci., vol. 31, no. 1, pp. 123

128, Feb. 2003.

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2006.


SIP0304-1

EYE BASED CURSOR MOVEMENT USING EEG IN

BRAIN COMPUTER INTERFACE Tariq S Khan

#, Mudassir Ali

#, Omar Farooq

#, Yusuf U Khan

*,

#Department of Electronics Engineering, Zakir Husain College of Engineering & Technology

*Department of Electrical Engineering, Zakir Husain College of Engineering & Technology

Aligarh Muslim University, Aligarh

Abstract The aim of this study is to

detect eye movement (left to right) from

Electroencephalograph (EEG) signal.

Four electrodes of EEG in the frontal

area were used. The statistical features

were extracted from the four channels of

frontal channel. These features were then

fed into a classifier based on the linear

discriminator function. The most

prominent features for the classification

of left and right movements were

identified. These features were then

interfaced with computer so that cursor

movement can be controlled. Electrodes

are placed along the scalp following the

10-20 International System of Electrode

Placement. Recorded data was filtered,

windowed and analysed in order to

extract features. Four different classifiers

were used. Best results were found in

support vector machine (SVM) and linear

classifiers each of which gave the average

accuracy of 90%.

Keywords: BCI, Eye movement, EEG.

I. INTRODUCTION

A brain-computer interface (BCI)

provides an alternative communication

channel between the human brain and a

computer by using pattern recognition

methods to convert brain waves into control

signals. Patients who suffer from severe

motor impairments (severe cerebral palsy,

head trauma and spinal injuries) may use

such a BCI system as an alternative form of

communication by mental activity [1]. Using

improved measurement devices, computer

power, and software, multidisciplinary

research teams in medicine,

psychophysiology, medical engineering, and

information technology are investigating and

realizing new noninvasive methods to

monitor and even control human physical

functions.

In a bigger picture there can be devices

that would allow severely disabled people to

function independently. For a quadriplegic,

something as basic as controlling a computer

cursor via mental commands would

represent a revolutionary improvement in

quality of life. With an EEG or implant in

place, the subject would visualize closing his

or her eyes or moving eyes from left to right

and vice versa [2]. The software can learn

eye movement through training, using

repeated trials. Subsequently, the classifier

may be used to instruct the closure/opening

of eye. A similar method is used to

manipulate a computer cursor, with the

subject thinking about forward, left, right

and back movements of the cursor [3]. With

enough practice, users can gain enough

control over a cursor to draw a circle, access

computer programs and control a television.

It could theoretically be expanded to allow


2011

SIP0304-2

users to "type" with their thoughts. This can

be achieved by controlling cursor movement

on a computer screen through EEG signals

from brain, specifically, generated due to

eye movement. The signals can be analysed

by different methods.

Traditional analysis methods, such as the

Fourier Transform and autoregressive

modelling are not suitable for non-stationary

signals. Recently, wavelets have been used

in numerous applications for a variety of

purposes in various fields. It is a logical way

to represent and analyse a non-stationary

signal with variable sized region windows

and to provide local information. In the

Fourier Transform (FT), the time

information is lost and in short Term Fourier

Transform (STFT) there is limited time

frequency resolution. Even though basic

filters can be used for decomposition of

desired bands, ideal filters are never realised

in practice, which results in aliasing effects.

However, wavelet analysis enables perfect

decomposition of the desired bands, which

helps us to obtain better features [4].

In this paper different features are used

for training the classifier for eye movement

in left and right directions. A time-frequency

analysis was applied to the EEG signals

from different channels, to determine

combination of features and channels that

yielded the best classification performance.

II. BACKGROUND RESEARCH

EEG waves are created by the firing of

neurons in the brain and were first measured

by Vladimir Pravdich-Neminsky who

measured the electrical activity in the brains

of dogs in 1912, although the term he used

was electrocerebrogram. Ten years later

Hans Berger became the first to measure

EEG waves in humans, in addition to giving

them their modern name, began what would

become intense research in utilizing these

electrical measurements in the fields of

neuroscience and psychology.

The term Brain-Computer Interface first

appeared in scientific literature in the 1970's,

though the idea of hooking up the mind to

computers was nothing new [5]. Currently,

the systems are open loop and responds to

users thoughts only. The closed loop

systems are aimed to be developed that can

give feedback to user as well.

In order to meet the requirements of the

growing technology expansion, some kind of

standardization was required not only for the

guidance of future researchers but also for

the validation and checking of new

developments with other systems, thus a

general purpose system was developed

called BCI2000 which made analysis of

brain siganl recording easy by defining the

output formats and operating protocols to

facilitate the researchers in developing any

type of application. This made it easier to

extract specific features of brain activity and

translate them into device control signals

[7]..

III. OUR METHODOLOGY

The procedure in this study was to initially

acquire EEG data. The stored data was then

pre-processed to remove artifacts.

Subsquently features were extracted in the

clean EEG and used for classification. Thus

methodology is shown in Fig. 1.

Fig. 1: Block diagram for feature extraction and device

control of eye movement

Data acquisition

Data Processing

Feature Extraction

Classification

Device/Application Control


2011

SIP0304-3

A.Experimental Setup and Data Acquisition

The subject was seated on wooden armchair

and legs were rested on wooden footrest

(wooden items should be used so as to

reduce interference) with eyes closed. The

subject was instructed to avoid speaking and

to avoid body movement in order to ensure

relaxed body. EEG data were recorded using

a Brain Tech clarityTM

system [9] with the

electrodes positioned according to the

standard 10-20 system in the biomedical

Signal Processing lab, AMU Aligarh.

To ensure the same rate of eye movement in

both directions, a ball was shown on the

screen and the subject was asked to visually

follow the ball. The movement of ball was

set to 60 pixels per second. A series of trials

were recorded.

The subject was instructed to open eyes

slowly and then to follow the movement of

the ball in the program on prompt from the

experimenter. Movement of eyes was

recorded for two different directions i.e. left

to right and right to left. Block diagram of

experimental procedure is shown in fig. 2.

Fig. 2 Sequence followed during experimental recording

B. Data Processing

26 channels of EEG were recorded. Since

only frontal lobe is mainly involved in eye

movement, only those channels that are

associated with the frontal lobe i.e. FP1-F3,

FP1-F7, FP2-F4, FP2-F8 were analysed. The

signal values associated with these signals

were extracted in ASCII form using

BrainTech software. EEG of the frontal lobe

channels for subject 1 is illustrated in Fig. 3.

Fig. 3: Plot of channels associated with frontal lobe

50 Hz power supply often causes

interference in the EEG recording. Fig. 4

shows a plot of PSD on the EEG record of

FP1-F3 channel. To eliminate these spikes

signal was passed through Infinite Impulse

Response (IIR) notch filter before analysis.

Fig. 4: Power Spectral Density of FP1F3

IIR second order notch filter with the

quality factor (or Q factor) of 3.91 was used

to remove the undesired frequency

components.

Signal after removing the artifacts of 4

channels stacked over one another is shown

in Fig. 5.

Relax

Left to

right

movement

Relax

Right to

left

movement

50 100 150 200 250 300 350 400 450 500

0

500

1000

1500

No. of Samples

Am

pli

tud

e

Frontal lobe channels

fp1f3

fp1f7

fp2f4

fp2f7

0 20 40 60 80 100 120 140-40

-30

-20

-10

0

10

20

30

Frequency (Hz)

Po

wer

(dB

)

PSD before & after Passing Through Notch

PSD of FP2F8

PSD after passing

through Notch Filter


2011

SIP0304-4

Fig. 5: Signal Plot of filtered Frontal lobe associated

channels

EEG by nature is non stationary signal. So it

was fragmented into frames so that it can be

assumed stationary for small segment. EEG

data is divided into frames of 1s duration i.e.

frame size of 256 samples.

C. Feature extraction

Feature extraction is the process of

discarding the irrelevant information to the

possible extent and representing relevant data

in a compact and meaningful form. Two eye

movements were recorded: right to left

(RTL), left to right (LTR).Standard statistical

parameters such as mean, variance,

skewness, cross-correlation were calculated

for all the channels in each movement type.

D. Classification

Following classifiers were used to classify

the two eye movements:

SVM: It is non-probabilistic binary linear

classifier.

Linear: Fits a multivariate normal density to

each group, with a pooled estimate of

covariance.

Diaglinear: Similar to 'linear', but with a

diagonal covariance matrix estimate (naive

Bayes classifiers).

Quadratic: Fits multivariate normal densities

with covariance estimates stratified by

group.

E. Cursor Control

A program was written which controls the

cursor movement according to instruction

given. This program will be calibrated

according the instructions given i.e. the

cursor movement will be invoked instead of

mouse movement as the instruction same as

that of the mouse movement. This

instruction will then be interfaced with the

eye movement which will then control the

movement of cursor [9].

IV. RESULTS AND DISCUSSIONS

For each frame of EEG, four features were

calculated namely, variance, mean, skewness

and cross correlation. The seperabrability

provided by each feature was individually

tested. The best three features were

subsequently used as an input to the

classifier. Four classifiers were used in this

work. The classifiers results are illustrated in

Table 1.For each movement of LTR and

RTL 20 seconds (20 frames) of data were

collected. From these 20 frames 15 frames

were used for training and rest 5 are used for

testing for both movements.

Table 1: Percentage accuracy of classification for eye

movements

Classifier RTL LTR

SVM 80 100

Linear 80 100

Quad 60 40

Diaglinear 80 60

From the observations in Table1 it can be

seen that linear or SVM classifier gives the

best possible results with high classification

percentage accuracy for both eye

movements.

0 100 200 300 400 500-500

0

500

1000

1500

2000Signal after passing through Notch filter

fp1f3

fp1f7

fp2f4

fp2f8


2011

SIP0304-5

Fig. 6: Plot of Classifier in Signal Space

A linear classifier classifying both eye

movements is shown in Fig. 6.

Fig. 7: Variance Plot of FP2-F4

From Fig. 6 which shows the variance for the

channel FP2-F4 clearly shows that the

variance of LTR is greater than RTL for

most of the time. Variance basically shows

the concentration of probability density

function about the mean.

V. CONCLUSIONS

EEG data was investigated for two eye

movements using a 4 channel setup on three

subjects. Features were extracted from the

variance for both the movements. A linear

classifier was used to classify between the

two eye movements. These algorithms can

provide high classification accuracy only

after training for few sessions. In this work

90% of accuracy has been achieved, in

classifying the two movements (RTL &

LTR).

ACKNOWLEDGEMENT

The authors are indebted to the UGC. This work is a part of the funded major research project C.F. No 32-14/2006(SR)

.

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2. Y. U. Khan,(2010) Imagined wrist movement classification in single trial EEG for brain computer interface using wavelet

packet, Int. J. Biomedical Engineering and Technology, Vol. 4, No. 2, pp169-180.

3. Daniel, J. Szafir (2009-10) Non-Invasive BCI through EEG An Exploration of the Utilization of electroencephalography to Create

Thought-Based Brain-Computer Interfaces.

4. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M. (2002): Braincomputer interfaces for communication and control. Clinical Neurophys. pp767791

5. Y. U. Khan and O. Farooq(2009), Autoregressive features based classification for seizure detection using neural network in scalp

Electroencephalogram, International Journal of Biomedical

Engineering and Technology, vol.2, no. 4, pp. 370-381.

6. J. Vidal(1973) "Toward Direct BrainComputer Communication." Annual Review of Biophysics and Bioengineering. Vol. 2, pp. 157-

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7. Syed M.Siddique, Laraib Hassan Siddique (2009): EEG based Brain computer Interface: Journal of software, vol.4, no.6, pp.550-

555

8. EEG Channels in Detecting Wrist Movement Direction Intention: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems

9. Fabiani, Georg E. et al. Conversion of EEG activity into cursor movement by a brain-computer interface.

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10. Clarity Braintech system, Standard edition, Software version 3.4, Hardware version 1.4, Clarity Medical Private Limited

-15 -10 -5 0 5 10 15 2010

20

30

40

50

60

RTL

LTR

Support Vectors

Classifier

0 5 10 15 200

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Time(sec)

Va

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LTR

seminar topics

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