Exploring the effect of urban features and immediate environment on body responses

Emotional Responses to the Visual Patterns of Urban Streets: Evidence from Physiological and Subjective Indicators

“…EDA reflects the autonomic sympathetic arousal integrated with emotional and cognitive states [57,58], whose signals can be characterized by a slowly varying tonic activity (i.e., skin conductance level (SCL)) and a fast varying phasic activity (i.e., skin conductance responses (SCRs)) [59]. As SCRs are sudden rises in the skin conductivity in response to an increase in sympathetic activity, they are often used to measure event-related phasic changes linked to emotional stimuli [60], in which SCR shows a steep incline to the peak and a slow decline to the baseline [60]. After correction for the motion artifact [61], we used the Ledalab tool in MATLAB to decompose the EDA data streams into SCL and SCRs [59,62].…”

Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring

“…Another workable way to lower the number, cost and weight of sensors is employing phone-based sensors and applications (43.6%), for example, phone-inbuilt temperature sensors [47], microphone to measure noise [28], and Bluetooth to measure the social distance [35]. In addition, phone-based application can assist the measurement in the field [41] and control data [46], such as "ExpoApp" [42], which is an integrated system to assess multiple personal environmental exposures.…”

“…For studies employing sensors with many variations of frequency, it is critical to decide the sampling rate for fusing data; 15.4% employed a moving average to decrease the possible loss of resolution. For example, in Benita and Tunçer [28], they use a moving average (f = 1 Hz) to link up temperature, wind and atmospheric pressure (f = 0.5 Hz), phone-based GPS and speed (f = 0.2 Hz), EDA data (f = 4 Hz) and noise (f = 10 Hz). In the reviewed papers, the window size for moving average varies from 1 min [44,64] to 5 min [44], which is dependent on the data features.…”

“…Benefitting from GPS data, over 50% of reviewed papers aggregated data over spatial units, such as based by 20 m × 20 m cellular network [62], 50 m grid squares [64], microenvironment types [63] and specific Locations with 50 m/100 m buffers [36]. Having spatial data, some papers utilized spatial clustering algorithms such as Hotspot analysis [27,28], Getis-Ord Gi* local statistics and kernel density [38,58] to understand spatial features of environmental effects in Geography Information System (GIS).…”

Data-driven thinking for measuring the human experience in the built environment

“…With rapid global urbanization, landscape architects, urban designers, and urban planning agencies alike have been increasingly recognizing the relevance of understanding the relationships between the surrounding built environment and human well-being. Several environmental factors impact our physical responses, 7,9,10 hence, by combining “on-body sensors” and environmental sensors, researchers from physiological and affective computing are doing important and valuable efforts in classifying emotional experiences and correlating them with the built environment. 8 For instance, the data generated by heart rate belts, accelerometers, and other devices that measure changes in blood volume, electrodermal activity, and body temperature can be used to detect objective measurements of stress patterns.…”