This study aimed to provide a multiangled comparison of the impact of real and virtual setups on the behaviour and perception of electric scooter (e-scooter) riding to examine the external validity of the virtual environments in the micro-mobility context. For this purpose, a within-subject design experiment was conducted to collect data on the behaviour of e-scooter riders. Furthermore, self-reported data on mental and physical demand as well as physiological data in the form of heart rate measurements and electroencephalography (EEG) were recorded to provide an additional insight into the behavioural results. The analysis was based on the multinomial logit model (MNL) for the behavioural data, ordered logit models for self-reported measures. Parametric and non-parametric tests were performed to capture the differences in physiological data. The results of the behavioural data showed significant differences in braking and acceleration patterns between virtual and real scenarios, which undermined the external validity of virtual environments in the current context. Further, self-reported measures painted a mixed picture when looked at jointly with biometric measures, where the questionnaires indicated that both setups were indifferent with respect to mental demand, while the physiological data suggested that virtual scenarios were more mentally engaging for the e-scooter riders.

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.

När världen går in intelligensåldern är det ingen överraskning att det är enklare än någonsin att samla in en så stor variation av datatyper, vare sig det är rådata om mänskligt beteende eller om bearbetade data om resmönster, analyserad med hjälp av artificiell intelligens, kan denna data visa sig vara otroligt värdefull.

De datainsamlingsverktyg som har blivit tillgängliga inom områdena människa-datorinteraktion och människa-maskininteraktion under de senaste åren visar stor potential för att avslöja hur trafikanter påverkas av omgivningen de färdas genom eller verkar i. Det är inte bara möjligt att noggrant samla in hur en person interagerar med ett fordon, vare sig det är ett konventionellt eller ett fjärrstyrt fordon, utan det är också möjligt att förstå psykologisk och fysiologisk effekt av fordonets omgivning på personen. Sådana biometriska data härrör mest från hjärn- och hjärtaktivitet, därav titeln på denna avhandling: Put your heart into it.

Denna avhandling utforskar hur biometrisk och beteendedata kan samlas in, vilka metoder som bör användas för analys och hur experiment bör utformas för att optimera potentialen hos den insamlade datan. Genom tre studier, var och en med fokus på fotgängare, elsparkcyklar och allmän informationssökning för förare, är denna avhandling tänkt som ett första steg mot en guide för andra underdomäner inom transport, som psykologi och teknik, om hur man samlar in och analyserar psykologisk information genom fysiska data, även kallad psykofysiologi.

Den första studien fokuserar på longitudinella studier med lågfrekvent biometriska data insamlad genom smartklockor. Ger grövre resultat i termer av psykologisk analys men visar sig vara ganska icke-påträngande eftersom deltagarna kan slå på och stänga av datainsamlingen efter egen vilja.

Den andra studien använder högfrekvent biometriska data, insamlad genom bröstband och elektrodhjälmar. Resultaten ger mer exakta avläsningar, vilket leder till analyser som ger mer djupgående information om hur en persons kognitiva belastning och riskuppfattning påverkas av omgivningen och deras egna handlingar.

Den sista studien jämför de biometriska och beteendemässiga egenskaperna för att söka information i både statiska och dynamiska scenarier. Med hjälp av blickspårning och huvudrörelser, använder denna studie enkla dataanalyser för att visa hur viktigt blickspårning kan vara när syftet är att förstå vad en trafikant tittar på när de försöker söka information, oavsett om de är statiska eller rör sig längs en väg.

Sammanfattningsvis har dessa studier och denna avhandling inte bara bevisat hur användbara och effektiva dessa datainsamlingsmetoder kan vara, utan också tagit tidiga steg för att avslöja vad som fungerar och vad som inte fungerar när man skapar scenarier för studier med syfte att förstå hur trafikanter påverkas i den nuvarande transportmiljön.

Nanofibrous filter materials were prepared by electrospinning a solution of 28 wt% poly(vinylidene fluoride) in N,N-dimethylacetamide with and without the addition of 2 wt% AgNO3, Cu(NO3)(2)center dot 2.5H(2)O or ZnCl2. X-ray diffraction, scanning electron microscopy with energy dispersive X-ray spectroscopy, inductively coupled plasma mass spectroscopy, thermogravimetric analysis, contact angle measurement, nitrogen sorption, and mercury intrusion porosimetry methods were used for the characterization of physical structure as well as the chemical composition of the electrospun materials. Particle filtration efficiency and antiviral activity against the SARS-CoV-2 alpha variant were tested in order to estimate the suitability of the prepared electrospun filter mate-rials for application as indoor air filtration systems with virucidal properties. All filter materials prepared with salts demonstrated very high particle filtration efficiency (>= 98.0%). The best antiviral activity was demonstrated by a material containing Cu(NO3)(2)center dot 2.5H(2)O in the spinning solution, which displayed the decrease in the number of infectious virions by three orders of magnitude after a contact time of 12 h. Materials with the addition of AgNO3 and ZnCl2 decreased the number of infectious virions after the same contact time by only-8 and-11 times, respectively.

To better understand the interactions between physical built environment conditions and one’s well-being, we created a passive data collector for travellers and made the first step towards an explanatory model based on psychophysiological relations. By measuring biometric information from select trial participants we showed how different controlled factors are affecting the heart rate of the participants. A regression model with the impact factors such as speed, location, time and activity (accelerometer data) reveals how the factors relate to each other and how they correlate with the recorded individual’s heart rates throughout the observed period. For examples, the results show that the increase in movement speed is not linearly correlated with the heart rate. One’s heart rate would increase significantly when the individual reaches brisk walking and running speed, but not before nor after. Early morning and early evening time slots were the time where the observed individuals have the highest heart rates, which may correlate to individuals’ commute activities. Heart rates at the office would be lower than at home, which might correlate to more physical activities in the household. 

Travel surveys can uncover information regarding travel behaviour, needs, and more. Collected information is utilised to make choices when reorganising or planning built environments. Over the years, methods for conducting travel surveys have changed from interviews and forms to automated travel diaries in order to monitor trips made by travellers. With the fast progression of technological advancements, new possibilities for operationalising such travel diaries can be implemented, changing from utilising mobile to wearable devices. Wearable devices are often equipped with sensors which collect continuous biometric data from sources that are not reachable from standard mobile devices. Data collected through wearable devices range from heart rate and blood pressure to temperature and perspiration. This advancement opens new possible layers of information in the collection of travel data. Such biometric data can be used to derive psychophysiological conditions related to cognitive load, which can uncover in-depth knowledge regarding stress and emotions. This paper aims to explore the possibilities of data analysis on the data collected through a software combining travel survey data, such as position and time, with heartrate, to gain knowledge of the implications of such data. The knowledge about the implications of spatial configurations can be used to create more accessible environments.

Steering feedback is one essential aspect to provide real world information, and can influence driving performance during remote driving. In this work, the classical feedback models based on physical characteristics (Physical Model) and modular characteristics (Modular Model) of the steering system are constructed separately, and the influences of it on the remote drivers are studied. Objective and subjective measurement methods are separately used for evaluating the performance of the feedback models. In the subjective assessment, a multi-level assessment method is used for studying the influence of steering models on driver’s intuitive feeling. In the objective assessment, lane following accuracy, steering reversal rates, vehicle speed, time consumption, and throttle engagement are studied for different feedback models and scenarios. Moreover, the human biological information of electroencephalogram and heart rate variability are measured for studying the workload differences. The results showed that the physical model gave drivers a better steering characteristic feel and confidence in remote driving while the modular model could provide better real world feel. Returnability was an important parameter in remote driving, and the level of feedback force and returnability speed could be lower in remote driving compared to real car driving. It was also found that drivers had a higher workload in remote driving compared to real car driving.

Age related cognitive diseases are becoming a growing problem in Sweden. With the fast ageing population and lowered mortality rate comes the spread of cognitive diseases related to dementia. In order to accommodate this growing target group in transport and the built environment, it is important to understand the mobility and travel behaviour of patients suffering from these diseases. One subset of this target group is travellers suffering from age induced illnesses related with dementia, which most often have fluctuating symptoms that are affecting the cognitive skills of the traveller. This makes it hard to use standardized forms and survey-based information that would require the traveller to actively respond retroactively, either in oral or written form, since the traveller might have forgotten or mixed up their past experiences, among other things, it becomes very hard to gain confidence in the results as it might be hard to tell in which condition the patient is during the collection.

We propose an automated collection of biometric data such as heart rate in combination with position. Since the validity of the information collected in this manner is directly related to the quality of the sensors used it means that the precision and accuracy of the results could be virtually endlessly improved by upgrading the hardware and optimizing the software. To take a first step towards a solution like this we have started developing a smart watch application which is utilizing PPG technology to collect heart rate and combine it with positions collected through GPS technology.

Early testing has shown the possibility to correlate the heart rate of a traveller to their specific location. The implications of this must be validated through data labelling as we wish to utilize machine learning algorithms to analyse the data collected.

Travel surveys has been used for decades to observe the patterns, locations, and choices, which travellers chose and do during the given observed period. This information can be utilized as background for informed planning decisions. Despite the progress in the travel survey technologies, the applications mostly focus on more traditional travel parameters. With programmable smart watches now, we can also collect real time data that is not solely pertaining to position and travel mode choices, but also to users’ biometric data. Such an application would open another level of possibilities in dynamically integrating land use and transport planning with public health research.

Utilising a smart watch platform, we are aiming to develop a tool that will collect biometric data, in combination with spatial context, such as position, spatial features and objects in the built environment, and by utilizing machine learning algorithms, try to detect how travellers are affected by their choice of transport mode, the built environment in general as well as how the public transport is operated.

Early testing reveals the possibility to find correlations between heart rate and position, which in turn could reveal the effect of spatial and transportation elements on the traveller. By targeting widely available hardware, the scalability for this tool is virtually endless, making it possible to collect large amounts of data and utilizing machine learning algorithms to analyse it.

The life span of the inhabitants of Sweden is increasing and with this comes age related cognitive diseases such as those related to dementia. Our society is not prepared to accommodate for the needs of the people who are affected by this.

The diseases related to dementia often affect the person’s ability to localize themselves and to remember previous and upcoming events. A common issue that occurs is a state called “elopement”.

Virtual characters are an integral part of many game and learning environments and have practical applications as tutors, demonstrators or even representations of the user. However, creating virtual character behaviors can be a time-consuming and complex task requiring substantial technical expertise. To accelerate and better enable the use of virtual characters in social games, we present a virtual character behavior toolkit for the development of expressive virtual characters. It is a midlleware toolkit which sits on top of the game engine with a focus on providing high-level character behaviors to quickly create social games. The toolkit can be adapted to a wide range of scenarios related to social interactions with individuals and groups at multiple distances in the virtual environment and supports customization and control of facial expressions, body animations and group formations. We describe the design of the toolkit, providing an examplar of a small game that is being created with it and our intended future work on the system.