There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.

A continued shift of human mobility towards sustainable and active mobility modes is a major concern for society in order to reduce the human contribution to climate change as well as to improve liveability and health in urban environments. For this change to succeed, non-motorized modes of transport need to become more attractive. Cycling can play a substantial role for short to medium distances, but perceived safety and stress levels are still major concerns for cyclists. Therefore, a quantitative assessment of cyclists’ stress sensations constitutes a valuable input for urban planning and for optimized routing providing low-stress routes. This paper aims to investigate stress sensations of cyclists through quantifying physiological measurements and their spatial correlation as an intersubjective indicator for perceived bikeability. We developed an automated workflow for stress detection and aggregation, and validated it in a case study in the city of Salzburg, Austria. Our results show that measured stress generally matches reported stress perception and can thus be considered a valuable addition to mobility planning processes.

Fundamentals and Principles of Musical Telepresence

Although modern computer networks do provide very high data rates and very low network jitter, consumer-grade systems for low-latency media streaming and audio conferencing still need research. Not only the network itself but also the timing jitter at the sending and receiving endpoints may cause discontinuities in the media stream. Hence, the use of jitter buffering at the receiver side is mandatory. To keep the end-to-end latency low, the average number of samples in the jitter buffer should firstly be as small as possible and secondly, constant over time. In practice, the latter requirement is very difficult to fulfill: As the sender and receiver do not share a common sampling clock, clock drift causes a slight deviation between the number of samples recorded by the sender and the number played by the receiver in a certain time interval. This effect causes frequent buffer under- or overruns if an extremely small jitter buffer is used. In this paper, the authors present their novel audio streaming solution that aims to achieve end-to-end latencies lower than 20 milliseconds and uses a tunable hardware oscillator circuit to compensate clock drift.

Cycling experiences a remarkable renaissance as an everyday mode of transport and in an increasing number of cities, cycling substantially contributes to the overall traffic. However, cyclists are not a homogeneous group of road users, but very diverse in terms of behavior, motivators, and deterrents. In order to gain better insights into driving forces and behavior patterns of cyclists, we conducted an opt-in online survey, in which socio-demographic, lifestyle, and mobility behavior data were collected. In total, 1234 responses with a completion rate of 87% (1073 complete survey) were collected between 3 May and 3 June 2019. With reference to complete responses, the gender ratio is balanced (53% female) and the mean age is 42 (σ = 12.75). A relative majority of participants cycles frequently. The fully anonymized dataset contains 107 data points per response, including survey metadata.

Walking is recognised as a healthy and sustainable mode of transport. Providing adequate infrastructure is pivotal for the promotion of walking and, subsequently, for achieving the benefits derived from its numerous positive effects. However, efficiently measuring the walkability at the street level remains challenging. In this paper, we present an indicator-based assessment model that can be used with open spatial data to evaluate segment-based walkability. The model incorporates eleven indicators describing the street segments and their close surroundings that are relevant for pedestrians, such as the presence and type of pedestrian infrastructure, road category, noise levels, and exposure to green and blue space. A weighted average calculation results in walkability index values for each street segment within a road network graph. The model’s generic approach and the ability to be used with open data ensure its reproducibility, adaptability, and scalability. The feasibility of the walkability model was shown using a case study for Salzburg, Austria. The model’s validity was evaluated through a large-scale study involving 660 full responses to an online survey. Participants provided ratings on the walkability of randomly selected street segments in Salzburg, which were compared with the calculated index, revealing a strong correlation (Spearman’s rank correlation = 0.82).