Journal cover Journal topic
Advances in Cartography and GIScience of the ICA
Journal topic
Volume 2
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 2, 9, 2019
https://doi.org/10.5194/ica-adv-2-9-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 2, 9, 2019
https://doi.org/10.5194/ica-adv-2-9-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Nov 2019

06 Nov 2019

Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

Kalliopi Kyriakou1 and Bernd Resch1,2 Kalliopi Kyriakou and Bernd Resch
  • 1Department of Geoinformatics – Z_GIS, University of Salzburg, 5020 Salzburg, Austria
  • 2Center for Geographic Analysis, Harvard University, Cambridge, MA, USA

Keywords: spatial patterns, stress detection, wearables

Abstract. Over the last years, we have witnessed an increasing interest in urban health research using physiological sensors. There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, most of the studies focus mainly on the analysis of the physiological signals and disregard the spatial analysis of the extracted geo-located emotions. Methodologically, the use of hotspot maps created through point density analysis dominates in previous studies, but this method may lead to inaccurate or misleading detection of high-intensity stress clusters. This paper proposes a methodology for the spatial analysis of moments of stress (MOS). In a first step, MOS are identified through a rule-based algorithm analysing galvanic skin response and skin temperature measured by low-cost wearable physiological sensors. For the spatial analysis, we introduce a MOS ratio for the geo-located detected MOS. This ratio normalises the detected MOS in nearby areas over all the available records for the area. Then, the MOS ratio is fed into a hot spot analysis to identify hot and cold spots. To validate our methodology, we carried out two real-world field studies to evaluate the accuracy of our approach. We show that the proposed approach is able to identify spatial patterns in urban areas that correspond to self-reported stress.

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