Paper Info
Title
Augmenting gesture recognition with erlang-cox models to identify neurological disorders in premature babies
Abstract
In this paper we demonstrate a Markov model based technique for recognizing gestures from accelerometers that explicitly represents duration. We do this by embedding an Erlang-Cox state transition model, which has been shown to accurately represent the first three moments of a general distribution, within a Dynamic Bayesian Network (DBN). The transition probabilities in the DBN can be learned via Expectation-Maximization or by using closed-form solutions. We test this modeling technique on 10 hours of data collected from accelerometers worn by babies pre-categorized as high-risk in the Newborn Intensive Care Unit (NICU) at UCI. We show that by treating instantaneous machine learning classification values as observations and explicitly modeling duration, we improve the recognition of Cramped Synchronized General Movements, a motion highly correlated with an eventual diagnosis of Cerebral Palsy.
Year
DOI
Venue
2012
10.1145/2370216.2370278
UbiComp
Keywords
DocType
Citations 
augmenting gesture recognition,dynamic bayesian network,closed-form solution,neurological disorder,cramped synchronized general movements,erlang-cox model,modeling technique,erlang-cox state transition model,markov model,classification value,cerebral palsy,premature baby,newborn intensive care unit,transition probability,sensors,gesture recognition,health,user modeling
Conference
3
PageRank 
References 
Authors
0.41
30
4
Name
Order
Citations
PageRank
Mingming Fan14810.29
Dana Gravem2141.23
Dan M. Cooper3141.23
Donald J. Patterson41765219.99