Put your heart into it
What biometrics and behaviour can teach us about road users
Time: Thu 2025-08-28 10.00
Location: F3 (Flodis), Lindstedtsvägen 26 & 28, Stockholm
Video link: https://kth-se.zoom.us/j/62096219683
Language: English
Subject area: Machine Design
Doctoral student: Robin C. O. Palmberg , Integrated Transport Research Lab, ITRL, Maskinkonstruktion
Opponent: Assist. Prof. Yan Feng, Delft University of Technology, Delft, The Netherlands
Supervisor: Un Mikael Nybacka, Integrated Transport Research Lab, ITRL, Fordonsteknik och akustik; Universitets lektor Gyözö Gidofalvi, Geoinformatik, Integrated Transport Research Lab, ITRL; Professor Yusak Susilo, Institute of Transport Studies, Universität für Bodenkultur Wien (University of Natural Resources and Life Sciences), Vienna, Austria
Abstract
As the world enters the intelligence age, it is no surprise that it is easier than ever to collect a vast variation of data types, whether it be raw data on human behaviour or processed data on travel patterns, that can be analysed with the help of artificial intelligence and prove to be incredibly valuable. In the domain of transport science, there is a constant quest to make the transport sector safer and more efficient without hindering those travelling from getting to where they need to be, when they need to be there, and data is vital in that quest.
The data collection tools that have become available in the fields of human-computer interaction and human-machine interaction over the past couple of years show great potential in uncovering how road users are affected by the surroundings they travel through or operate in. It is not only possible to meticulously collect how a person is interacting with a vehicle, be it a conventional or a remotely controlled vehicle, but it is also possible to collect biometric data to understand the psychological and physiological effects of the surroundings on the person. Such biometric data mostly stem from brain and heart activity, hence the title of this thesis: Put your heart into it.
This thesis explores how biometric and behavioural data can be collected, which methods should be used for analysis, and how experiments should be designed to optimize the potential of the data sets collected. Through three studies, focusing each on pedestrians, electric scooters, and general driver information searching, this thesis is intended as a first step towards a guide for other subdomains within transport, like psychology and engineering, on how to collect and analyse psychological information through physical data, also called psychophysiology.
The first study focuses on longitudinal studies with low-frequency biometric data collected through smartwatches. Providing cruder results in terms of psychological analysis but proving rather non-intrusive since the participants can turn on and turn off the data collection at their own will.
The second study utilises high-frequency biometric data, collected through chest straps and electrode helmets. The results provide more accurate readings, leading to analyses that provide more in-depth information regarding how a person’s cognitive load and risk perception is affected by their surroundings and their own actions.
The last study compares the biometric and behavioural characteristics of searching for information in both static and dynamic scenarios. Using eye-tracking and head-movement, this study uses simple data analyses to show how important eye-tracking can be when the aim is to understand what a road user is looking at when trying to search for information, whether they are static or moving along a road.
In conclusion, these studies and this thesis have not only proven how useful and efficient these data collection methods can be, but also taken early steps to uncover how to create studies that allow for these data collection methods to be trialled with the aim to understand how road users are affected in the current transport environment.