Understanding real-world phenomena from human-generated sensor data

Tsapeli, Theofania Kleio (2018). Understanding real-world phenomena from human-generated sensor data. University of Birmingham. Ph.D.

PDF - Accepted Version

Download (1MB)


Nowadays, there is an increasing data availability. Smartphones, wearable devices, social media, web browsing information and sales recordings are only few of the newly available information sources. Analysing this kind of information is an important step towards understanding human behaviour. In this dissertation, I propose novel techniques for uncovering the complex dependencies between factors extracted from raw sensor data and real-world phenomena and I demonstrate the potential of utilising the vast amount of human digital traces in order to better understand human behaviour and factors influenced by it. In particular, two main problems are considered: 1) whether there is a dependency between social media data and traded assets prices and 2) how smartphone sensor data can be used to understand factors that influence our stress level. In this thesis, I focus on uncovering the structural dependencies among factors of interest rather than on the detection of mere correlation. Special attention is given on enhancing the reliability of the findings by developing techniques that can better handle the specific characteristics of the examined datasets. Although the developed approaches are motivated by specific problems related to human-generated sensor data, they are general and can be applied in any dataset with similar characteristics.

Type of Work: Thesis (Doctorates > Ph.D.)
Award Type: Doctorates > Ph.D.
College/Faculty: Colleges (2008 onwards) > College of Engineering & Physical Sciences
School or Department: School of Computer Science
Funders: None/not applicable
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
URI: http://etheses.bham.ac.uk/id/eprint/8445


Request a Correction Request a Correction
View Item View Item


Downloads per month over past year