Authors:
E. Pattyn
1
;
2
;
E. Lutin
1
;
2
;
A. Van Kraaij
3
;
N. Thammasan
3
;
D. Tourolle
3
;
I. Kosunen
3
;
D. Tump
3
;
W. De Raedt
2
and
C. Van Hoof
1
;
2
;
3
Affiliations:
1
Department of Electrical Engineering, KU Leuven, Leuven, Belgium
;
2
Imec, Leuven, Belgium
;
3
OnePlanet Research Centre, Wageningen, The Netherlands
Keyword(s):
Affective Computing, Feature Extraction, Physiology, Signal Processing Algorithms, Wearable Sensors.
Abstract:
Electrodermal activity (EDA) reflects changes in electrical conductivity of the skin via activation of the
sympathetic nervous system. Ambulatory EDA measurements bring multiple challenges regarding quality
assessment and response detection. A signal quality indicator (SQI) is one method to overcome these. This
study aimed to investigate the transferability and generalizability of several open-source state-of-the-art SQIs
and response detectors regarding their performance against manually annotated EDA of participants in rest.
Three annotators identified artifacts and physiological responses in wrist EDA of 45 participants (10.75 hours).
The F1-score, precision, and recall of several state-of-the-art SQIs and response detectors were computed on
a subset of the annotated data (n=28). The SQIs and response detectors resulted in F1 scores between 3-16%
and 18-32%, respectively. These results indicated that current SQIs and response indicators are not performant
enough for EDA of subject
s in rest, implying similar or worse outcomes for ambulatory EDA. It is suggested
that SQIs must be adjusted based on the used device and set-up.
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