664 research outputs found
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data
One of the main benefits of a wrist-worn computer is its ability to collect a
variety of physiological data in a minimally intrusive manner. Among these
data, electrodermal activity (EDA) is readily collected and provides a window
into a person's emotional and sympathetic responses. EDA data collected using a
wearable wristband are easily influenced by motion artifacts (MAs) that may
significantly distort the data and degrade the quality of analyses performed on
the data if not identified and removed. Prior work has demonstrated that MAs
can be successfully detected using supervised machine learning algorithms on a
small data set collected in a lab setting. In this paper, we demonstrate that
unsupervised learning algorithms perform competitively with supervised
algorithms for detecting MAs on EDA data collected in both a lab-based setting
and a real-world setting comprising about 23 hours of data. We also find,
somewhat surprisingly, that incorporating accelerometer data as well as EDA
improves detection accuracy only slightly for supervised algorithms and
significantly degrades the accuracy of unsupervised algorithms.Comment: To appear at International Symposium on Wearable Computers (ISWC)
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EngageMe: The Design and Implementation of a Reflective Tool for Evaluating Student Engagement
Recently, there has been a growing push to explore the potential of non-cognitive factors in helping students reach their fullest potential. Engagement, one predictor of student achievement, is such a factor. Because the conditions under which engagement is elicited may vary, EngageMe, a visualization tool, has been developed to assist instructors’ efforts to understand student engagement in the learning process. The application attempts to enhance traditional observation methods by utilizing electrodermal activity, a measure of physiological arousal, as a proximal indicator of engagement. An iterative, participatory design process was used to create prototypes of the EngageMe interface. The results of this design process, a study focused on the barriers to adoption of this kind of technology, as well as an exploratory case study are discussed. Finally, implications for future development are presented
A usability study of physiological measurement in school using wearable sensors
Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps
Wavelet-based motion artifact removal for electrodermal activity
Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data
TOBE: Tangible Out-of-Body Experience
We propose a toolkit for creating Tangible Out-of-Body Experiences: exposing
the inner states of users using physiological signals such as heart rate or
brain activity. Tobe can take the form of a tangible avatar displaying live
physiological readings to reflect on ourselves and others. Such a toolkit could
be used by researchers and designers to create a multitude of potential
tangible applications, including (but not limited to) educational tools about
Science Technologies Engineering and Mathematics (STEM) and cognitive science,
medical applications or entertainment and social experiences with one or
several users or Tobes involved. Through a co-design approach, we investigated
how everyday people picture their physiology and we validated the acceptability
of Tobe in a scientific museum. We also give a practical example where two
users relax together, with insights on how Tobe helped them to synchronize
their signals and share a moment
Detecting deception and suspicion in dyadic game interactions
In this paper we focus on detection of deception and suspicion from
electrodermal activity (EDA) measured on left and right wrists during
a dyadic game interaction. We aim to answer three research
questions: (i) Is it possible to reliably distinguish deception from
truth based on EDA measurements during a dyadic game interaction?
(ii) Is it possible to reliably distinguish the state of suspicion
from trust based on EDA measurements during a card game?
(iii) What is the relative importance of EDA measured on left and
right wrists? To answer our research questions we conducted a
study in which 20 participants were playing the game Cheat in
pairs with one EDA sensor placed on each of their wrists. Our
experimental results show that EDA measures from left and right
wrists provide more information for suspicion detection than for
deception detection and that the person-dependent detection is
more reliable than the person-independent detection. In particular,
classifying the EDA signal with Support Vector Machine (SVM)
yields accuracies of 52% and 57% for person-independent prediction
of deception and suspicion respectively, and 63% and 76% for
person-dependent prediction of deception and suspicion respectively.
Also, we found that: (i) the optimal interval of informative
EDA signal for deception detection is about 1 s while it is around
3.5 s for suspicion detection; (ii) the EDA signal relevant for deception/
suspicion detection can be captured after around 3.0 seconds
after a stimulus occurrence regardless of the stimulus type (deception/
truthfulness/suspicion/trust); and that (iii) features extracted
from EDA from both wrists are important for classification of both
deception and suspicion. To the best of our knowledge, this is the
firstwork that uses EDA data to automatically detect both deception
and suspicion in a dyadic game interaction setting.N
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