Colour coding based novel data representation and lightweight convolutional neural network architecture for hierarchical anomaly detection on ehealth applications

Date

2020-10

Author

Yatbaz, Hakan Yekta

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eHealth is on its way to become an essential industry due to the advancements in information technology. Human activity recognition (HAR) is one of the most popular areas within the scope of eHealth, particularly with applications in anomaly detection. Although there are various studies on HAR, most of them propose complex models that are not compatible with portable devices and wearables due to their restricted computing capabilities. In this thesis, new data representation is presented along with a lightweight convolutional neural network (CNN) for this purpose. An anomaly detection framework is presented, which uses ECG data for heart effort prediction of daily life activities. The novel data representation approach and the proposed deep learning model are tested on the MHEALTH dataset with two different validation techniques for accuracy and three different complexity metrics. The results show that the proposed approaches can achieve up to 96.92% and 97.06% accuracy for HAR, and heart effort level with five fold cross validation. In addition, the models proposed for inertial data based and ECG based predictions have sizes of 0.89 MB and 1.97 MB and have a complexity of 0.06 and 1.04 Giga FLOPS, respectively.

Subject Keywords

eHealth, Human Activity Recognition, Lightweight, Deep Learning

URI

https://hdl.handle.net/11511/69236

Collections

Northern Cyprus Campus, Thesis

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Citation Formats

H. Y. Yatbaz, “Colour coding based novel data representation and lightweight convolutional neural network architecture for hierarchical anomaly detection on ehealth applications,” M.S. - Master of Science, Middle East Technical University, 2020.