Delineating the Operational Envelope of Mobile and Conventional EDA Sensing
on Key Body Locations
Medical Device Sensing
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Tsiamyrtzis, Panagiotis
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Dcosta, Malcolm
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Shastri, Dvijesh
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Prasad, Eswar
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Pavlidis, Ioannis
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.5665-5674
© Copyright 2016 ACM
Summary: Electrodermal activity (EDA) is an important affective indicator, measured
conventionally on the fingers with desktop sensing instruments. Recently, a new
generation of wearable, battery-powered EDA devices came into being,
encouraging the migration of EDA sensing to other body locations. To
investigate the implications of such sensor/location shifts in
psychophysiological studies we performed a validation experiment. In this
experiment we used startle stimuli to instantaneously arouse the sympathetic
system of n=23 subjects while sitting. Startle stimuli are standard but minimal
stressors, and thus ideal for determining the sensor and location resolution
limit. The experiment revealed that precise measurement of small EDA responses
on the fingers and palm is feasible either with conventional or mobile EDA
sensors. By contrast, precise measurement of small EDA responses on the sole is
challenging, while on the wrist even detection of such responses is problematic
for both EDA modalities. Given that affective wristbands have emerged as the
dominant form of EDA sensing, researchers should beware of these limitations.
Efficient Text Classification Using Best Feature Selection and Combination
of Methods
Interacting with Information, Documents and Knowledge
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Srinivas, Mettu
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Supreethi, K. Pujari
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Prasad, E. V.
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Kumari, S. Anitha
HIMI 2009: Human Interface and the Management of Information, Symposium on
Human Interface, Part I: Designing Information Environments
2009-07-19
v.1
p.437-446
Copyright © 2009 Springer-Verlag
Summary: Lsquare and k-NN classifiers are two machine learning approaches for text
classification. Rocchio is the classic method for text classification in
information retrieval. Our approach is a supervised method, meaning that the
list of categories should be defined and a set of training data should be
provided for training the system. In this approach, documents are represented
as vectors where each component is associated with a particular word. We
propose voting method and OWA operator and Decision Template method for
combining classifiers. In these we use an effective and efficient new method
called variance-mean based feature filtering method of feature selection. Best
feature selection method and combination of methods are used to do feature
reduction in the representation phase of text classification is proposed. Using
this efficient feature selection method and best classifier combination method
we improve the text classification performance.
