EMPhASIS - Status update

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This document outlines the EMPhASIS project which aims to develop an embedded public attention stress identification system. It presents the roadmap and design overview, including extracting time and frequency features from ECG signals using trapezoidal integration and Fourier transforms. These features, like mean RR interval and LF/HF ratio, are then classified to predict stress levels. The core algorithms could also be reused for applications like mortality risk assessment or emotion detection. Overall, the document provides an overview of the EMPhASIS project goals, design approach, and potential applications of their stress identification system.

Education

EMPhaSIS:
EMbedded Public Attention Stress
Identification System
Asia Ciallella: asia.ciallella@mail.polimi.it
Jessica Leoni: jessica.leoni@mail.polimi.it
19th of June 2019
DEIB Seminar Room

3
Road Map
Literature
Analysis
Market
Analysis

4
Road Map
Software
Implementation
Literature
Analysis
Market
Analysis

5
Road Map
Software
Implementation
Literature
Analysis
Market
Analysis
Hardware
Implementation

6
Road Map
Software
Implementation
Literature
Analysis
Market
Analysis
Final
Report
Hardware
Implementation

9
Design Overview
Trapezoidal
Integration
Fourier
Transform
Trapezoidal
Integration
LF/(LF+HF)
LF/HF
HF/(LF+HF)
(HF-LF)/(HF+LF)
Peaks Finder
RR
Calculation
mRR
mHR
HR
Calculation
Classifier
RR
HR
Peaks mRR
mHR
nLF
nHF
SVI
dLFHF
RR
RR
Calibration

10
Trapezoidal
Integration
Fourier
Transform
Trapezoidal
Integration
LF/(LF+HF)
LF/HF
HF/(LF+HF)
(HF-LF)/(HF+LF)
Peaks Finder
RR
Calculation
mRR
mHR
HR
Calculation
Classifier
RR
HR
Peaks mRR
mHR
nLF
nHF
SVI
dLFHF
RR
RR
Calibration
Hardware Implementation Overview

11
Time Features Exctraction
A sliding window of 16
samples length is
applied to the signal.
For each window are
calculated:
𝒎𝑹𝑹𝒊 =
σ 𝑘=1
16
𝑅𝑅 𝑘
16
𝒎𝑯𝑹𝒊 =
60000
σ 𝑘=1
16
𝑅𝑅 𝑘

12
Trapezoidal Integration
Leveraging integral
calculation is estimated
area under low
frequency and high
frequency regions¹:
𝑳𝑭𝒊 = න
0.04
0.15
𝑆 𝑘 𝑑𝑘
𝑯𝑭𝒊 = න
0.15
0.40
𝑆 𝑘 𝑑𝑘
[1] A. Malliani, F. Lombardi, and M. Pagani, “Power spectrum analysis of heart rate variability: a tool to explore neural regulatory
mechanisms.” British heart journal.

13
Frequency Features Exctraction
Once known LF and HF area,
frequency features can be
calculated:
𝑛𝐿𝐹𝑖 =
𝐿𝐹𝑖
𝐿𝐹𝑖 + 𝐻𝐹𝑖
𝑛𝐻𝐹𝑖 =
𝐻𝐹𝑖
𝐿𝐹𝑖 + 𝐻𝐹𝑖
𝑆𝑉𝐼𝑖 =
𝐿𝐹𝑖
𝐻𝐹𝑖
𝑑𝐿𝐹𝐻𝐹𝑖 = |𝐿𝐹𝑖 − 𝐻𝐹𝑖|

14
Classification
Prediction
Calibration
Time
Features
Frequency
Features

15
Reusability of the Core
[2] M. J. Lipinski, G. W. Vetrovec, and V. F. Froelicher, “Importance of the first two minutes of heart rate recovery after exercise treadmill
testing in predicting mortality and the presence of coronary artery disease in men,” The American journal of cardiology, vol. 93, no. 4.
[3] F. Agrafioti, D. Hatzinakos, and A. K. Anderson, “Ecg pattern analysis for emotion detection,” IEEE Transactions on affettive computing.
Mortality
Risk and
Coronary
Disease
Predictor²
Daily
Routine
Monitor
Emotion
Detector³

16
Road Map
Software
Implementation
Literature
Analysis
Market
Analysis
Final
Report
Hardware
Implementation

17
So… What’s Left?
Literature
Analysis
Software
Implementation
Market
Analysis
Hardware
Implementation Final
Report

Thank you for your kind attention!
emphasis.xowh@gmail.com
emphasis.xowh
EMPhASIS
Contact us:

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EMPhASIS - Status update

1. EMPhaSIS: EMbedded Public Attention Stress Identification System Asia Ciallella: asia.ciallella@mail.polimi.it Jessica Leoni: jessica.leoni@mail.polimi.it 19th of June 2019 DEIB Seminar Room

2. 2 Road Map Literature Analysis

3. 3 Road Map Literature Analysis Market Analysis

4. 4 Road Map Software Implementation Literature Analysis Market Analysis

5. 5 Road Map Software Implementation Literature Analysis Market Analysis Hardware Implementation

6. 6 Road Map Software Implementation Literature Analysis Market Analysis Final Report Hardware Implementation

7. 7 Emphasis Project Overview

8. 8 Emphasis Project Overview

9. 9 Design Overview Trapezoidal Integration Fourier Transform Trapezoidal Integration LF/(LF+HF) LF/HF HF/(LF+HF) (HF-LF)/(HF+LF) Peaks Finder RR Calculation mRR mHR HR Calculation Classifier RR HR Peaks mRR mHR nLF nHF SVI dLFHF RR RR Calibration

10. 10 Trapezoidal Integration Fourier Transform Trapezoidal Integration LF/(LF+HF) LF/HF HF/(LF+HF) (HF-LF)/(HF+LF) Peaks Finder RR Calculation mRR mHR HR Calculation Classifier RR HR Peaks mRR mHR nLF nHF SVI dLFHF RR RR Calibration Hardware Implementation Overview

11. 11 Time Features Exctraction A sliding window of 16 samples length is applied to the signal. For each window are calculated: 𝒎𝑹𝑹𝒊 = σ 𝑘=1 16 𝑅𝑅 𝑘 16 𝒎𝑯𝑹𝒊 = 60000 σ 𝑘=1 16 𝑅𝑅 𝑘

12. 12 Trapezoidal Integration Leveraging integral calculation is estimated area under low frequency and high frequency regions¹: 𝑳𝑭𝒊 = න 0.04 0.15 𝑆 𝑘 𝑑𝑘 𝑯𝑭𝒊 = න 0.15 0.40 𝑆 𝑘 𝑑𝑘 [1] A. Malliani, F. Lombardi, and M. Pagani, “Power spectrum analysis of heart rate variability: a tool to explore neural regulatory mechanisms.” British heart journal.

13. 13 Frequency Features Exctraction Once known LF and HF area, frequency features can be calculated: 𝑛𝐿𝐹𝑖 = 𝐿𝐹𝑖 𝐿𝐹𝑖 + 𝐻𝐹𝑖 𝑛𝐻𝐹𝑖 = 𝐻𝐹𝑖 𝐿𝐹𝑖 + 𝐻𝐹𝑖 𝑆𝑉𝐼𝑖 = 𝐿𝐹𝑖 𝐻𝐹𝑖 𝑑𝐿𝐹𝐻𝐹𝑖 = |𝐿𝐹𝑖 − 𝐻𝐹𝑖|

14. 14 Classification Prediction Calibration Time Features Frequency Features

15. 15 Reusability of the Core [2] M. J. Lipinski, G. W. Vetrovec, and V. F. Froelicher, “Importance of the first two minutes of heart rate recovery after exercise treadmill testing in predicting mortality and the presence of coronary artery disease in men,” The American journal of cardiology, vol. 93, no. 4. [3] F. Agrafioti, D. Hatzinakos, and A. K. Anderson, “Ecg pattern analysis for emotion detection,” IEEE Transactions on affettive computing. Mortality Risk and Coronary Disease Predictor² Daily Routine Monitor Emotion Detector³

16. 16 Road Map Software Implementation Literature Analysis Market Analysis Final Report Hardware Implementation

17. 17 So… What’s Left? Literature Analysis Software Implementation Market Analysis Hardware Implementation Final Report

18. Thank you for your kind attention! emphasis.xowh@gmail.com emphasis.xowh EMPhASIS Contact us:

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