1. Phd Candidate Computational Physiology Lab University of Houston
George Panagopoulos
Phd Candidate
Computational Physiology Lab University of Houston

2. Phd Program in UH Degree Requirements: Prerequisite courses
4 core graduate courses
8 graduate courses
3 courses each semester
GPA >= 3.4
No grade less than B
Up to 24 to 30 credits from research
6 to 12 credits for dissertation

3. Lab’s orientation
“At this moment the lab has three research lines and an educational research effort on science ethics. Our focus is on unobtrusive and sustained monitoring of physiological variables.” *
My responsibilities:
Curation and Processing of experimental data
Statistical and Exploratory analysis to provide conclusions
Machine learning modeling *

4. Research Project Toyota Safety Research Project
Aim: Detect abnormal driving patterns, given measurements in physiological activity and evaluate ways to reduce the likelihood of these events and assist the driver to recover from them.
Two driving simulations and one track study with 67 subjects.
Recording physiological e.g. breathing, heart rate, perspiration etc. and driving activity e.g. acceleration, speed, breaking etc.
Apply numerous stressors, to cause physiological and driving perturbations and evaluate their connection.
Estimate the average behavioral differences between subjects.

5. Driving Simulation 1
Resampled ,visualized and evaluated the quality of the physiological recordings.
Repeated HRV Example (Subject 3, Sensorimotor Drive)
Histogram of recordings (signals) with at least one Zero or NA value, seperated by loaded drive type

6. Driving Simulation 1
2. Performed paired statistical tests to evaluate the significance of differences between the subjects’ activity during stressed and non stressed periods.
Loaded Phases in Heart Rate signal, for a Subject’s Baseline and Cognitive Drive
Driving Variables significant in terms of differences between Loaded and Baseline Drives

7. Driving Simulation 1
3. Used ordinary and total least squares models to evaluate the relationship between each driving variable and combinations of physiological variables.
R^2 (summed for all Drives) for ordinary least squares models predicting Speed, with one and three physiological time series aggregations
R^2 (summed for all Drives) for ordinary least squares models predicting Steering, with one and three physiological time series aggregations

8. Driving Simulation 1
4. Used granger causality and transfer entropy to asses potential causation relationship between heart rate and perinasal perspiration.
Granger Causality of the physiological signals, averaged over all subjects. The smaller the p values the more significant the results
Granger Causality of the initial signals and their delta, averaged over all lags for each subject, in each drive. The more p values under horizontal line, the more significant the result.

9. Driving Simulation 2
Gathered and organized 10 kinds of data, from 4 different sensors, for 66 subjects, 6 sessions each.
Created detailed analysis about the missing files.
Extracted the data segments relevant to the exact experimental sessions based on experimenter’s notes.
Normalization and resampling of the time series for visualization in subjectbook.*
Evaluated the quality of each data type separately, for faults in :
-Recording length,based on experimental design
-Sensor measurements, based on physiology
-Non synchronized time series *

10. Future Research Plan Evaluation of perspiration sensors
Measure the difference between sensor measurements in perinasal, wrist and palm area and evaluate their accuracy.
Effect of stress in physiological activity
Qualitative and quantitative evaluation of subjects’ physiological indications, under stressed and non stressed periods of driving.
Predict driving activity based on physiological activity.
Initial approach:
Recurrent Neural Network with 5 inputs ( physiological signals at time t), and 4 outputs ( driving signals at time t+1).

11. Thank you gpanagopoulos@uh. edu http://cpl. uh