Hello everyone, I am Shkurta Gashi and today
it is my pleasure to present our paper on detection of artifacts in ambulatory
electrodermal activity data. Mobile and wearable devices such as,
wristbands, are nowadays commonly used to track people's physiological parameters
like, heart rate and electrodermal activity.
Electrodermal activity represents the physiological arousal of a person during
an activity and it has been widely used to
infer people's behaviors such as, affective
states sleep quality, cognitive load and the
quality of social interactions. Recent advancements in ubiquitous
technologies allow continuous and unobtrusive measurement
of electrodermal activity data in real world scenarios. However such
data is seriously impacted by a major issue: the presence of artifacts,
which are caused by the recording procedure, by
environment temperature as well as body movements.
Thereby automatic recognition of artifacts in
ambulatory electrodermal activity data is crucial for humans
behavior detection systems to not only clean the data but also to avoid making
conclusions in spurious data and this is the goal of our paper.
Existing approaches in identifying artifacts in electrodermal
activity data include: visual inspection, which is
cumbersome and very time consuming, signal processing techniques, which
distort the signal and make genuine physiological responses
resemble artifacts or vice versa. Existing automatic approaches have
mainly been testing tested on data collected in a specific
setting or from a specific demographic group of
users and all of the existing approaches do
not consider the impact of contextual information for this problem.
Thereby in this paper we present our approach
on identifying artifacts and we propose to use multiple sensors,
not only electrodermal activity but also skin temperature and acceleration.
We collect a data set completely in the wild from 13 participants,
which includes students, teachers as well as
researchers. We collect around 2 000 hours of this data and two human
annotators annotate 100 hours of this data set.
We make both data sets publicly available.
We then also compile a list of guidelines and a dashboard in order
to enable researchers to manually label artifacts if it is needed. Both of
these tools are available in the following link.
In order to analyze the data, we follow common signal processing and machine
learning steps. In particular we first filter the
signal to remove a noise, we then decompose the signal
into the phasic and tonic components, we segment the data into five second
windows and we use these windows from both the mixed
electrodermal activity signal as well as the phasic components
as an input to the classification pipeline,
which is composed of the feature extraction. We
extract three types of features statistical,
peaks and wavelets features which are then used as input to different
classifiers to linear and non-linear classifiers as well as
ensemble and deep learning methods. We then resampled the data using the
random undersampling technique in order to combat the
imbalance data set. We rescale the features using the min
max scaler and we evaluate our approach by
leave one subject out as well as leave one day
out validation techniques. We also tune the parameters of the
best classifier using a grid search. Our main findings show that from the
different classifiers that we investigated, we obtained the best
performance using ensembl methods in particular gradient boosting
techniques. We then compare our approach with the
random classifiers and we find that we achieve a recall of 98 percent
for artifacts identification which is significantly higher than the
random guess classifier and this increment is
a 42 percentage points. We then achieve an accuracy of 97
percent which outperforms the biased classifier that always
predicts the majority class, the increment is by 30
percentage points. We further compare our approach with existing approaches in
the literature and we show that state-of-the-art approaches do not
generalize well for in the wild data, even after
manual adoption. We then train and test existing
approaches using our data sets and even in this case our approach
outperforms existing ones by eight percentage points.
We further quantify the impact of artifacts, body movements and temperature
change in electrodermal activity signals and
in particular we observe that eda features that are commonly used to
detect humans behavior are impacted by the
presence of artifacts, change in temperature environment as
well as a physical activity level of the user.
Thereby, we propose a new metric the, EDA quality index, to quantify
the overall quality of EDA signals based on these three factors :the
artifacts, the temperature change and body
movements. For more details please check out our
paper. Thank you for your attention!