Athena Project€¦ · Athena Project Jaime Ciriaco Michael Dunn Aaron Marquez Sonoma State...

Athena Project

Jaime Ciriaco

Michael Dunn

Aaron Marquez

Sonoma State University Department of Engineering Science

Advisor: Farid Farahmand

Client: Arthur Obuchowicz

http://athena490.wordpress.com

ciriaco@sonoma.edu

dunnmi@sonoma.edu

marqueaa@sonoma.edu

Introduction

• One of the most common disorders in the world is epilepsy

• It can be dangerous for someone with epilepsy to be left home alone because a seizure can leave them

incapacitated and unable to call for help

• This can be especially dangerous if certain appliances such as a stove or iron were left on

• There are only a few devices that monitor this disorder

• Efficient seizure detection algorithms are needed

• If a person has a seizure then they could be incapacitated for a period of time.

Hardware

• 9 Degree of Freedom Sensor: Accelerometer, Gyroscope, Magnetometer

• Feather M0 microcontroller

• Buzzer

Accelerometer/Gyroscope Data

• Accelerometer and Gyroscope data used for analysis

• Velocity (m/s^2) in x, y, z axes

• Rotational Velocity (degrees-per-second) in

x, y, z axes

• 20 Hz Sample Rate New sample every

50 ms

Accelerometer/Gyroscope Data

• Accelerometer and Gyroscope data used for analysis

VX(n) Vy(n), Vz(n)

VX(n) Vy(n), Vz(n), fx, fy, fz

Received Accelerometer Data

Seizure Event Random Movement

Vx(n) Vy(n), Vz(n)

Received Accelerometer Data

Seizure Event Random Movement

Vx(n) Vy(n), Vz(n)

Problem: How do you

detect Seizure?

Introducing Root-Mean-Square Analysis

• Read accelerometer & gyroscopic values

• Calculate RMS

V RMS =Vx

2 +Vy2 +Vz

2

3

RMS =1

Nvi

2

i

N

åGeneral Equation:

Our Implementation:

Seizure Detection Algorithm

• Calculate Difference: VRMS(n) – VRMS(n-1) > THRMS

• Calculate Counter: Count(n) = Count(n-1) + 1 > THC

Necessary Model

• Source: T. R. Burchfield and S. Venkatesan,"Accelerometer-Based Human

Abnormal Movement Detection in Wireless Sensor Networks”

THRMS

Our Model

• Green Arrow (THRMS): “Shaking event”

• Red Line (THC): “Possible Seizure Event”

>THRMS >THC

Including Fast Fourier Transform

in Seizure Detection Algorithm • Look at RMS data in frequency domain

X(k) = x( j)wN

( j-1)(k-1)

j=1

N

å

where

wN = e(-2pi)/N

FFTRMS (k) = VRMS ( j)wN

( j-1)(k-1)

j=1

N

å

K=Number of Samples

Seizure Detection Algorithm with FFT

If certain frequency found AND THC passed Alarm

FFT of RMS (VX, Vy, Vz) Data

FFTRMS (k) = VRMS ( j)wN

( j-1)(k-1)

j=1

N

å

FFT of Individual VX, Vy, Vz Data

FFT (k) = VX,Y ,Z ( j)wN

( j-1)(k-1)

j=1

N

å

FFT of Gyroscope (fx, fy, fz) Data

FFTx,y,z (k) = Fx, y, z( j)wN

( j-1)(k-1)

j=1

N

å

Final Seizure Algorithm

Conclusion

• Accuracy of model

• Speed of Model