Real-time physiological identification using incremental learning and semi-supervised learning

Abstract

The widespread usage of wearable sensors such as smart watches provide access to valuable objective physiological (such as Electrocardiogram(ECG)) signals ubiquitously. Healthcare domain has been tremendously benefited by the collection of physiological signals which can be used for health monitoring of patients. The signals from the wearable sensors enabled the researchers and data experts to process them and identify the human physiological state by classifying the human activities. This led to the growth and development of smart ecosystem in the healthcare domain.In this thesis, ECG signals have been investigated as the physiological measure to detect human activities. Various measures are extracted from ECG, such as heart rate variability, average heart rate etc. and their relationships with different human activities are investigated. To build a comprehensive analytical machine learning model for ECG signals and to enable the continuous monitoring of humans, one would need access to real time streaming of continuous data. So, the data would be unsupervised most of the time and it would be very expensive (almost practically impossible) to label all the data streaming in real time. Also, it is highly probable that the data is collected from different sessions and varying situations. Therefore, the machine learning models need to be able to adapt to new sessions. This would be a major challenge in human state monitoring provided that the conventional predictive models work only on the stationary data. Also, these models would fail to work on the data from multiple sessions. To provide a practical solution to address above issues, two advanced methods in machine learning have been discussed in this research: Incremental learning and Semi supervised learning. Incremental learning is a paradigm in Machine learning where the stream of input data is continuously used to extend the existing knowledge learnt by the model. The incremental learning module has been built in Apache Spark platform which provides a scalable cloud infrastructure to apply machine learning algorithms on streaming data. Semi supervised learning is another solution implemented in this thesis where some out of all the data points are labelled. Different semi supervised algorithms have been studied and applied which learn the relationship between features and adapts the model to data from multiple sessions. Finally, the results are compared and the implementation ideas for the discussed solutions have been proposed.