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.

No abstract

Abstract:In the face of the broad political call for an -energy turnaround‖, we are currently witnessing three essential trends with regard to energy infrastructure planning, energy generation and storage: from planned production towards fluctuating production on the basis of renewable energy sources, from centralized generation towards decentralized generation and from expensive energy carriers towards cost-free energy carriers. These changes necessitate considerable modifications of the energy infrastructure. Even though most of these modifications are inherently motivated by geospatial questions and challenges, the integration of energy system models and Geographic Information Systems (GIS) is still in its infancy. This paper analyzes the shortcomings of previous approaches in using GIS in renewable energy-related projects, extracts distinct challenges from these previous efforts and, finally, defines a set of core future research avenues for GIS-based energy infrastructure planning with a focus on the use of renewable energy. These future OPEN ACCESS ISPRS Int. J. Geo-Inf. 2014, 3 663 research avenues comprise the availability base data and their -geospatial awareness‖, the development of a generic and unified data model, the usage of volunteered geographic information (VGI) and crowdsourced data in analysis processes, the integration of 3D building models and 3D data analysis, the incorporation of network topologies into GIS, the harmonization of the heterogeneous views on aggregation issues in the fields of energy and GIS, fine-grained energy demand estimation from freely-available data sources, decentralized storage facility planning, the investigation of GIS-based public participation mechanisms, the transition from purely structural to operational planning, data privacy aspects and, finally, the development of a new dynamic power market design.Keywords: integration of GIS and energy system models; GIS and renewable energy; GIS-based energy infrastructure planning; future research challenges; fluctuating renewables; structural planning of local energy systems; operation optimization

This chapter introduces the 'Urban Emotions' approach. It focuses on integrating humans' emotional responses to the urban environment into planning processes. The approach is interdisciplinary and anthropocentric, i.e. citizens and citizens' perceptions are highlighted in this concept. To detect these emotions/perceptions, it combines methods from spatial planning, geoinformatics and computer linguistics to give a better understanding of how people perceive and respond to static and dynamic urban contexts in both time and geographical space. For collecting and analyzing data on the emotional perception to urban space, we use technical and human sensors as well as georeferenced social media posts, and extract contextual emotion information from them. The resulting novel information layer provides an additional, citizen-centric perspective for urban planners. In addition to technical and methodological aspects, data privacy issues and the potential of wearables are discussed in this chapter. Two case studies demonstrate the transferability of the approach into planning processes. This approach will potentially reveal new insights for the perception of geographical spaces in spatial planning.