Hi BERGers!
This week (Wednesday 26th September), we have Guillermo Hidalgo Gadea? giving a talk
titled "Measuring Fatigue and Comfort: An Experimental Approach"?.
This talk will combine 2 exciting studies, which you can read a bit about in the abstracts
below.
Looking forward to seeing you all there,
Gemma
[Image]
Abstract 1
This study aims to provide an interdisciplinary perspective on biosignal processing for
driver monitoring systems. Machine learning models were trained with ECG, Pupil Diameter
and Eyelid Opening data from overnight sleep deprived subjects experiencing microsleep in
a supervised test track driving environment. Model performance of 0.85 accuracy and 0.96
precision indicates (a) promising results for noninvasive microsleep detection in real
driving environment and (b) benefits of the presented interdisciplinary processing method
for psychological research.
Abstract 2
This paper presents the idea of brute force feature extraction for Electrocardiography
(ECG) signals applied to discomfort detection. To build an ECG Discomfort Corpus an
experimental discomfort induction was conducted. 50 subjects underwent a 2h (dis-)comfort
condition in separate sessions in randomized order. ECG and subjective discomfort was
recorded. 5min ECG segments were labeled with corresponding subjective discomfort ratings,
and 6365 brute force features (65 low-level descriptors, rst and second order derivatives,
and 47 function-als) and 11 traditional heart rate variability (HRV) parameters were
extracted. Random Forest machine learning algorithm outperformed SVM and kNN approaches
and achieved the best subject-dependent, 10-fold cross-validation results (r = .51). With
this experiment, we are able to show that (a) brute force ECG feature sets achieved better
discomfort detection than traditional HRV based ECG feature set; (b) cepstral and spectral
ux based features appear to be the most promising to capture HRV phenomena.?
Show replies by date