Baseline Modelling and Composite
Representation of Unobtrusively (IoT) Sensed
Behaviour Changes Related to Urban
Physical Well-Being
Vladimir Urošević
1(&) , Marina Andrić
1 , and José A. Pagán
2
1 Belit d.o.o. Beograd, Trg Nikole Pašića 9, 11000 Belgrade, Serbia
vladimir.urosevic@belit.co.rs
2 The New York Academy of Medicine, 1216 Fifth Avenue, New York
NY 10029, USA
Abstract. We present the grounding approach, deployment and preliminary
validation of the elementary devised model of physical well-being in urban
environments, summarizing the heterogeneous personal Big Data (on physical
activity/exercise, walking, cardio-respiratory fitness, quality of sleep and related
lifestyle and health habits and status, continuously collected for over a year
mainly through wearable IoT devices and survey instruments in 7 global testbed
cities) into 5 composite domain indicators/indexes convenient for interpretation
and use in predictive public health and preventive interventions. The approach is
based on systematized comprehensive domain knowledge implemented through
range/threshold-based rules from institutional and study recommendations,
combined with statistical methods, and will serve as a representative and performance benchmark for evolution and evaluation of more complex and
advanced well-being models for the aimed predictive analytics (incorporating
machine learning methods) in subsequent development underway.
Keywords: Behaviour recognition Á Wearable devices Á Unobtrusive sensing Á
Well-being Á Vital health parameters Á Data labelling Á Composite index
modelling
1 Introduction
The urban public health, well-being monitoring, and prevention are recently being
transformed from reactive to a predictive and eventually long-term risk mitigating
systems, through a number of research initiatives and projects, such as the ongoing
PULSE Project (Participative Urban Living in Sustainable Environments, funded from
the EU Horizon 2020 programme) focusing on the chronic metabolic and respiratory
diseases (such as type 2 diabetes and asthma) affected or exacerbated by the preventable or modifiable environmental and lifestyle factors, and well-being/resilience.
A major challenge in the Project is the modelling and assessment/prediction of citizen
well-being from the collected and processed Big Data of unprecedented variety and
from highly heterogeneous sources (health and vital activity personal data obtained
© The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 155–167, 2020.
https://doi.org/10.1007/978-3-030-51517-1_13
Representation of Unobtrusively (IoT) Sensed
Behaviour Changes Related to Urban
Physical Well-Being
Vladimir Urošević
1(&) , Marina Andrić
1 , and José A. Pagán
2
1 Belit d.o.o. Beograd, Trg Nikole Pašića 9, 11000 Belgrade, Serbia
vladimir.urosevic@belit.co.rs
2 The New York Academy of Medicine, 1216 Fifth Avenue, New York
NY 10029, USA
Abstract. We present the grounding approach, deployment and preliminary
validation of the elementary devised model of physical well-being in urban
environments, summarizing the heterogeneous personal Big Data (on physical
activity/exercise, walking, cardio-respiratory fitness, quality of sleep and related
lifestyle and health habits and status, continuously collected for over a year
mainly through wearable IoT devices and survey instruments in 7 global testbed
cities) into 5 composite domain indicators/indexes convenient for interpretation
and use in predictive public health and preventive interventions. The approach is
based on systematized comprehensive domain knowledge implemented through
range/threshold-based rules from institutional and study recommendations,
combined with statistical methods, and will serve as a representative and performance benchmark for evolution and evaluation of more complex and
advanced well-being models for the aimed predictive analytics (incorporating
machine learning methods) in subsequent development underway.
Keywords: Behaviour recognition Á Wearable devices Á Unobtrusive sensing Á
Well-being Á Vital health parameters Á Data labelling Á Composite index
modelling
1 Introduction
The urban public health, well-being monitoring, and prevention are recently being
transformed from reactive to a predictive and eventually long-term risk mitigating
systems, through a number of research initiatives and projects, such as the ongoing
PULSE Project (Participative Urban Living in Sustainable Environments, funded from
the EU Horizon 2020 programme) focusing on the chronic metabolic and respiratory
diseases (such as type 2 diabetes and asthma) affected or exacerbated by the preventable or modifiable environmental and lifestyle factors, and well-being/resilience.
A major challenge in the Project is the modelling and assessment/prediction of citizen
well-being from the collected and processed Big Data of unprecedented variety and
from highly heterogeneous sources (health and vital activity personal data obtained
© The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 155–167, 2020.
https://doi.org/10.1007/978-3-030-51517-1_13