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Computer Vision-Based Infant Movement Assessment

Rahmati, Hodjat
Doctoral thesis
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URI
http://hdl.handle.net/11250/2382254
Date
2016
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  • Institutt for teknisk kybernetikk [2240]
Abstract
The aim of this thesis is to develop an objective predictive model for cerebral

palsy based on video recording of spontaneous movements of young infants. In

the recent years, computer-based motion assessment methods have aimed to quantitatively

analyze infants’ movements in order to predict CP. However, they either

use extra instruments that are intrusive to the diagnosis task, or their results are

clinically hard to interpret. Thus, in order to tackle the problems with previous

methods and perform an analytic tool to study the disease, the study proposes a

new motion assessment tool to separate and track different body parts individually

and without need for any extra instrument.

In addition, the research achieves a model for prediction of cerebral palsy

based on motion data of young infants. The prediction is formulated as a classification

problem to assign each of the infants to one of the healthy or with cerebral

palsy group. The thesis studies the previously proposed features for predicting

cerebral palsy and in addition proposes a new feature set. Unlike formerly proposed

features that are mostly defined in the time domain, this study proposes a

set of features derived from frequency analysis of infants’ motions. Since cerebral

palsy affects the variability of the motions, suggested features are suitable and

consistent with the nature of the condition.

Finally, in the current application, a well-known problem, few subjects and

many features, was initially encountered. In such a case, most classifiers get trapped

in a sub-optimal model and, consequently, fail to provide sufficient prediction accuracy.

To solve this problem, a feature selection method that determines features

with significant predictive ability is proposed. The feature selection method decreases

the risk of false discovery and, therefore, the prediction model is more

likely to be valid and generalizable for future infants.
Publisher
NTNU
Series
Doctoral thesis at NTNU;2016:22

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