Time Series Movement Data represented as 2D image: Prediction of CP with Pretrained autoencoder
We look at how time series coordinate movement data can be represented as images which have the ability to be understood by both a non technological clinician and a deep learning architecture, more specific an autoencoder. Autoencoders are known as good feature extractors, a beneficial quality in order to recognise fidgety movement patterns made by infants. Fidgety movements are age specific movements, and absence of these are strongly related to cerebral palsy. Movement data is previously extracted using a version of Human Pose Estimation called CIMA Pose on the original video recordings of the infants. The vision of this project is to make a contribution for the InMotion project at St. Olav's University Hospital, which aims to develop an automatic computerbased CP predictor. Our main contributions focus on highlighting the relevant data features as 2D images and implementing an autoencoder for cerebral palsy classification. Findings made during the process include evaluation and insight about the proposed data representation together with a discussion and recommendations for future work. Autoencoder related findings cannot exclude the autoencoder as a feature extractor but indicates that more research is required. This master thesis concludes that the proposed data representation is not ideal, before recommending future work for the InMotion project, in order for them to develop an automatic CP prediction model that can give an early and reliable CP diagnosis.