By Topic

Involuntary gesture recognition for predicting cerebral palsy in high-risk infants

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

2 Author(s)
Singh, M. ; Sch. of Comput. Sci. & Inf., Univ. Coll. Dublin, Dublin, Ireland ; Patterson, D.J.

In this paper we describe a system that leverages accelerometers to recognize a particular involuntary gesture in babies that have been born preterm. These gestures, known as cramped-synchronized general movements are highly correlated with a diagnosis of Cerebral Palsy. In order to test our system we recorded data from 10 babies admitted to the newborn intensive care unit at the UCI Medical Center. We applied machine learning techniques to features based on their data and were able to obtain accuracies between 70% and 90% depending on the relative cost of false positives and false negatives. Validated video observation annotations were utilized as ground truth. Finally, we conducted an analysis to understand the basis of the algorithmic predictions.

Published in:

Wearable Computers (ISWC), 2010 International Symposium on

Date of Conference:

10-13 Oct. 2010