Doctoral defence: Hans Hõrak “Development of a computer vision-based privacy-preserving automatic observation method for measuring physical activity in school”

On 19 June at 10:00 Hans Hõrak will defend his doctoral thesis “Development of a computer vision-based privacy-preserving automatic observation method for measuring physical activity in school” for obtaining the degree of Doctor of Philosophy (in Sociology).

Supervisors:
Professor Triin Vihalemm, University of Tartu
Professor Gholamreza Anbarjafari, University of Tartu

Opponent:
Professor Arūnas Emeljanovas, Lithuanian Sports University (Lithuania)

Summary
How to observe people without seeing them?
They say it's not polite to stare. The right to privacy is considered a human right. However, there is much in human behavior that scientists would like to study via observation. For example, we want to know whether children will start moving more during recess if smartphones are banned at school? To figure this out, scientists would have to ask parental consent to carry out the observation. Assuming parents grant permission, a huge amount of labour would be needed for classical observation - several observers in the schoolhouse every day for a sufficiently long period before and after the smartphone ban. With my doctoral thesis, I tried to solve both the problem of privacy and of labor by replacing the human observer with artificial intelligence (AI).

Modern machine learning methods make it possible to train models that automatically detect objects and their properties in images or video. If we want an AI that recognizes people in images, we need to form a machine learning dataset with pictures of people and pictures without people. If we want an AI that differentiates between low and high physical activity in video, we need a corresponding video dataset. In my doctoral thesis, I collected a dataset where video of children's movement is synchronized with hip-worn accelerometers to train a model that could differentiate between lower and higher levels of physical activity in video. In collaboration with the ICV lab at the Institute of Technology, we developed a prototype video analysis sensor that can estimate the level of physical activity of people in the camera's field of view at real-time speed. The fact that AI can derive information about physical activity from the video without recording the footage or showing it to anyone at all, makes it possible to observe without seeing.

The method is designed for measuring physical activity in school-based research and therefore highly prioritizes privacy protection and research ethics. But more broadly, the thesis illustrates the potential of computer vision technologies for processing visual information in urban spaces and workplaces, and not only for measuring physical activity or adhering to high ethical standards. This warrants wider public discussion – under what conditions or whether at all is it OK to have a robot staring at you?