Smart Annotation using semi-supervised techniques

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Start date: 02/01/15
End date: 01/30/19


Objective health data about subjects outside of the laboratory is important in order to analyse symptoms that cannot be reproduced in the laboratory. A simple daily life example would be how stride length changes with tiredness or stress. In order to investigate this we must be able to accurately segment a stride from daily living data in order to have an accurate measure of duration and distance. State-of-the-art methods use separate segmentation and classification approaches. This is inaccurate for segmentation of an isolated activity, especially one that is not repeated. This could be solved using a model that is based on the sequence of phases within activities. Such a model is a graphical model. Currently we are working with Conditional Random Fields and Hierarchical Hidden Markov Models on daily living data. The applications will include sports as well as daily living activities.