How We Perceive Places: Measuring Biomarkers of Stress in Urban
Environments Using Personal Exposure Sensors, Deep Learning and
Functional Data Analysis

D 2024

How We Perceive Places: Measuring Biomarkers of Stress in Urban Environments Using Personal Exposure Sensors, Deep Learning and Functional Data Analysis

DVOŘÁK, Tadeáš, Markéta MAKAROVÁ, Devashish KHULBE, Radim LÍSKOVEC, Ondřej MALINA et. al.

Základní údaje

Originální název

How We Perceive Places: Measuring Biomarkers of Stress in Urban Environments Using Personal Exposure Sensors, Deep Learning and Functional Data Analysis

Autoři

Vydání

New York, SAP '24: ACM Symposium on Applied Perception 2024, od s. 1-3, 3 s. 2024

Nakladatel

Association for Computing Machinery, Inc

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10511 Environmental sciences

Stát vydavatele

Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Organizační jednotka

Přírodovědecká fakulta

ISBN

979-8-4007-1061-2

DOI

http://dx.doi.org/10.1145/3675231.3678876

UT WoS

001325273000020

Klíčová slova anglicky

urban environment; stress; high-level visual features; image segmentation; LSTM neural networks; functional data analysis

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 12. 2024 10:53, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

This study investigates the impact of visual stimuli and other environmental factors in urban settings on physiological markers of stress. Forty-four participants walked a predefined urban route while wearable sensors recorded street-level noise, air pollutants, and video footage. Stress responses were measured via heart rate, heart rate variability, and electrodermal activity. Urban scenes were categorized using the SegFormer model, and an LSTM model was applied to predict stress markers. Functional data analysis provided an interpretable model of heart rate responses to environmental variables. Data collection and analyses are ongoing.

Návaznosti

EF17_043/0009632, projekt VaV

Název: CETOCOEN Excellence

LM2023069, projekt VaV

Název: Výzkumná infrastruktura RECETOX

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Výzkumná infrastruktura RECETOX

MUNI/G/1249/2021, interní kód MU

Název: Stress of Citizens and Entropy in Urban Setting (Akronym: STRECITY)

Investor: Masarykova univerzita, Stress of Citizens and Entropy in Urban Setting, INTERDISCIPLINARY - Mezioborové výzkumné projekty

857560, interní kód MU
(Kód CEP: EF17_043/0009632)

Název: CETOCOEN Excellence (Akronym: CETOCOEN Excellence)

Investor: Evropská unie, CETOCOEN Excellence, Spreading excellence and widening participation

Citovat

DVOŘÁK, Tadeáš, Markéta MAKAROVÁ, Devashish KHULBE, Radim LÍSKOVEC, Ondřej MALINA, David GORNÝ, Pavel MORCINEK, Tomáš POMPA, Ondřej MIKEŠ, Veronika ECLEROVÁ, Filip ZLÁMAL, Stanislav SOBOLEVSKY, Ondřej MULÍČEK a Julie DOBROVOLNÁ. How We Perceive Places: Measuring Biomarkers of Stress in Urban Environments Using Personal Exposure Sensors, Deep Learning and Functional Data Analysis. In Rachel McDonnell, Lauren Buck, Julien Pettré, Manfred Lau, Göksu Yamaç. SAP '24: ACM Symposium on Applied Perception 2024. New York: Association for Computing Machinery, Inc, 2024, s. 1-3. ISBN 979-8-4007-1061-2. Dostupné z: https://dx.doi.org/10.1145/3675231.3678876.

@inproceedings{2429701,
   author = {Dvořák, Tadeáš and Makarová, Markéta and Khulbe, Devashish and Lískovec, Radim and Malina, Ondřej and Gorný, David and Morcinek, Pavel and Pompa, Tomáš and Mikeš, Ondřej and Eclerová, Veronika and Zlámal, Filip and Sobolevsky, Stanislav and Mulíček, Ondřej and Dobrovolná, Julie},
   address = {New York},
   booktitle = {SAP '24: ACM Symposium on Applied Perception 2024},
   doi = {http://dx.doi.org/10.1145/3675231.3678876},
   editor = {Rachel McDonnell, Lauren Buck, Julien Pettré, Manfred Lau, Göksu Yamaç},
   keywords = {urban environment; stress; high-level visual features; image segmentation; LSTM neural networks; functional data analysis},
   language = {eng},
   location = {New York},
   isbn = {979-8-4007-1061-2},
   pages = {1-3},
   publisher = {Association for Computing Machinery, Inc},
   title = {How We Perceive Places: Measuring Biomarkers of Stress in Urban Environments Using Personal Exposure Sensors, Deep Learning and Functional Data Analysis},
   url = {https://dl.acm.org/doi/10.1145/3675231.3678876},
   year = {2024}
}

TY  - CONF
ID  - 2429701
AU  - Dvořák, Tadeáš - Makarová, Markéta - Khulbe, Devashish - Lískovec, Radim - Malina, Ondřej - Gorný, David - Morcinek, Pavel - Pompa, Tomáš - Mikeš, Ondřej - Eclerová, Veronika - Zlámal, Filip - Sobolevsky, Stanislav - Mulíček, Ondřej - Dobrovolná, Julie
PY  - 2024
TI  - How We Perceive Places: Measuring Biomarkers of Stress in Urban Environments Using Personal Exposure Sensors, Deep Learning and Functional Data Analysis
PB  - Association for Computing Machinery, Inc
CY  - New York
SN  - 9798400710612
KW  - urban environment
KW  - stress
KW  - high-level visual features
KW  - image segmentation
KW  - LSTM neural networks
KW  - functional data analysis
UR  - https://dl.acm.org/doi/10.1145/3675231.3678876
N2  - This study investigates the impact of visual stimuli and other environmental factors in urban settings on physiological markers of stress. Forty-four participants walked a predefined urban route while wearable sensors recorded street-level noise, air pollutants, and video footage. Stress responses were measured via heart rate, heart rate variability, and electrodermal activity. Urban scenes were categorized using the SegFormer model, and an LSTM model was applied to predict stress markers. Functional data analysis provided an interpretable model of heart rate responses to environmental variables. Data collection and analyses are ongoing.
ER  -

DVOŘÁK, Tadeáš, Markéta MAKAROVÁ, Devashish KHULBE, Radim LÍSKOVEC, Ondřej MALINA, David GORNÝ, Pavel MORCINEK, Tomáš POMPA, Ondřej MIKEŠ, Veronika ECLEROVÁ, Filip ZLÁMAL, Stanislav SOBOLEVSKY, Ondřej MULÍČEK a Julie DOBROVOLNÁ. How We Perceive Places: Measuring Biomarkers of Stress in Urban Environments Using Personal Exposure Sensors, Deep Learning and Functional Data Analysis. In Rachel McDonnell, Lauren Buck, Julien Pettré, Manfred Lau, Göksu Yama\c c. \textit{SAP '24: ACM Symposium on Applied Perception 2024}. New York: Association for Computing Machinery, Inc, 2024, s.~1-3. ISBN~979-8-4007-1061-2. Dostupné z: https://dx.doi.org/10.1145/3675231.3678876.

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