TY - GEN
T1 - Baseline Modelling and Composite Representation of Unobtrusively (IoT) Sensed Behaviour Changes Related to Urban Physical Well-Being
AU - Urošević, Vladimir
AU - Andrić, Marina
AU - Pagán, José A.
N1 - Funding Information:
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement No. 727816 (PULSE). The performed research studies have all been granted ethical approval from the relevant IRB authority in each PULSE pilot testbed city (Ethics Committee of the Parc de Salut Mar hospital in Barcelona, through NHS Health Research Authority IRAS (Integrated Research Application System) in Birmingham, New York Academy of Medicine IRB in New York City, etc.), resulting from comprehensive multimonthly evaluation processes. The inclusion and exclusion methods and criteria for recruiting citizens have been specified in relevant previous publications of the Project, such as in Section 2.2.1. Participation Criteria in [23].
Publisher Copyright:
© 2020, The Author(s).
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Behaviour recognition
KW - Composite index modelling
KW - Data labelling
KW - Unobtrusive sensing
KW - Vital health parameters
KW - Wearable devices
KW - Well-being
UR - http://www.scopus.com/inward/record.url?scp=85087769342&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-51517-1_13
DO - 10.1007/978-3-030-51517-1_13
M3 - Conference contribution
AN - SCOPUS:85087769342
SN - 9783030515164
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 155
EP - 167
BT - The Impact of Digital Technologies on Public Health in Developed and Developing Countries - 18th International Conference, ICOST 2020, Proceedings
A2 - Jmaiel, Mohamed
A2 - Aloulou, Hamdi
A2 - Mokhtari, Mounir
A2 - Abdulrazak, Bessam
A2 - Kallel, Slim
PB - Springer
T2 - 18th International Conference on Smart Homes and Health Telematics, ICOST 2020
Y2 - 24 June 2020 through 26 June 2020
ER -