An analysis of fear of crime using multimodal measurement. (March 2018)

• Record Type: Journal Article
• Authors: Kim, Seul-Kee; Kang, Hang-Bong
• Is Part Of: Biomedical signal processing and control. Volume 41(2018)
• Page Start: 186
• Page End: 197
• Publication Date: 2018-03
• Subjects: Fear of crime -- EEG -- ECG -- GSR; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.12.003
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: In order to approach the fear of crime (FOC) on a more individual basis, we examined the influence of environmental and individual factors on the FOC. To objectively divide the level of fear of crime, we analyzed the features of the measured physiological signals (EEG, ECG, and GSR). The FOC was influenced by not only the environmental factors but also the physiological signals according to an individual's level of FOC. We proposed a new method to objectively measure the fear of crime to overcome the limitations of previous studies. Abstract: Fear of crime, which may be present without experiencing an actual crime, can restrict one's daily physical and mental activities and reduce quality of life. In previous research, fear of crime was measured by regional surveys. Though useful for confirming group characteristics, regional surveys cannot measure in real-time, assess individual characteristics, or provide an objective measure of anxiety. Since the causes and effects of fear of crime are highly individualized, we have developed a protocol to measure physiological signals in concert with existing surveys; this system can verify an individual's fear of crime characteristics in real time. Subjects were shown 6 clips of actual pedestrian environments (day/night of a commercial street scene, day/night of a residential street scene, day/night of a natural street scene). To ease immersion of the subjects into the scenes, clips were produced from the subjects' first-person point of view. Subjects were divided into two groups (Group1: N = 14, age, 22 ± 1.66 years; Group2: N = 13, age 21 ± 1.35 years) based on their fear intensity as reported on our pre-recording survey; electroencephalographic (EEG), electrocardiographic (ECG), and galvanic skin response (GSR) signals were compared between groups. They were then assessed via video for comparative purposes. Our results demonstrated that the physiological signals were dependent on how conscious an individual was of his or her own fear of crime. We found significant differences between the two groups for all video clips except for daytime commercial street and nighttime natural street; these data suggest that individual characteristics are important in measuring fear of crime.