By Topic

Support Vector Machines to Define and Detect Agitation Transition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Sakr, G.E. ; Dept. of Electr. & Comput. Eng., American Univ. of Beirut, Beirut, Lebanon ; Elhajj, I.H. ; Huijer, H.A.-S.

The need to automate the detection of agitation and the detection of agitation transition for dementia patients is a significant facilitator for caregivers. This research aims at detecting the transitional phase toward agitation, as well as agitation detection of subjects, using soft computing techniques that do not require supervision beyond the training phase. Three vital signs are monitored: Heart Rate (HR), Galvanic Skin Response (GSR), and Skin Temperature (ST). These measures are fed into two proposed SVM architectures which are based on the definition of a new confidence measure: "Confidence-Based SVM” and "Confidence-Based Multilevel SVM.” Results show very high detection accuracy of agitation and agitation transition, a quick adaptation to the subject, and a strong correlation between the physiological signals monitored and the emotional states of the subjects. Another challenge that is successfully addressed in this paper is the ability to train the classifier on a limited group of subjects, and then test it on subjects not belonging to the training group. The result is a learning algorithm that is "Subject-Independent.”

Published in:

Affective Computing, IEEE Transactions on  (Volume:1 ,  Issue: 2 )