Developing a Novel Citizen-Scientist Smartphone App for Collecting Behavioral and Affective Data from Children Populations

Maramis, Christos; Ioakimidis, Ioannis; Kilintzis, Vassilis; Stefanopoulos, Leandros; Lekka, Eirini; Papapanagiotou, Vasileios; Diou, Christos; Delopoulos, Anastasios; Kassari, Penio; Charmandari, Evangelia; Maglaveras, Nikolaos

doi:10.1007/978-3-030-49289-2_23

Cited by 12 publications

(16 citation statements)

References 9 publications

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“…For example, project-related efforts have already resulted in the quantification of different rates of ultra-processed food marketing in neighborhoods of Stockholm, Sweden, with more such food advertisements being present around subway stations in areas with lower socioeconomic population profiles [38]. Comprehensive models of the obesity prevalence dependence matrix are being created [39], allowing for the first time the data-driven effectiveness predictions about specific policies on a community and the real-time monitoring of the population response, supported by powerful data visualizations.…”

Section: Discussionmentioning

confidence: 99%

Novel e-Health Applications for the Management of Cardiometabolic Risk Factors in Children and Adolescents in Greece

et al. 2020

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Obesity in childhood and adolescence represents a major health problem. Novel e-Health technologies have been developed in order to provide a comprehensive and personalized plan of action for the prevention and management of overweight and obesity in childhood and adolescence. We used information and communication technologies to develop a “National Registry for the Prevention and Management of Overweight and Obesity” in order to register online children and adolescents nationwide, and to guide pediatricians and general practitioners regarding the management of overweight or obese subjects. Furthermore, intelligent multi-level information systems and specialized artificial intelligence algorithms are being developed with a view to offering precision and personalized medical management to obese or overweight subjects. Moreover, the Big Data against Childhood Obesity platform records behavioral data objectively by using inertial sensors and Global Positioning System (GPS) and combines them with data of the environment, in order to assess the full contextual framework that is associated with increased body mass index (BMI). Finally, a computerized decision-support tool was developed to assist pediatric health care professionals in delivering personalized nutrition and lifestyle optimization advice to overweight or obese children and their families. These e-Health applications are expected to play an important role in the management of overweight and obesity in childhood and adolescence.

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Section: Discussionmentioning

confidence: 99%

Novel e-Health Applications for the Management of Cardiometabolic Risk Factors in Children and Adolescents in Greece

et al. 2020

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“…A conceptual overview of the BigO system is shown in Figure 1. Data is collected from smartwatch and smartphone sensors through the "myBigO" app, available for Android and iOS operating systems [9]. It is then stored at the BigO DBMS, consisting of Apache Cassandra (for time series data) and MongoDB (for all other application data) databases.…”

Section: System Overviewmentioning

confidence: 99%

“…727688, 01/12/2016 -30/11/2020 (http://bigoprogram.eu/). 1 Aristotle University of Thessaloniki, Greece 2 Karolinska Institutet, Sweden 3 Biomedical Research Foundation of the Academy of Athens, Greece 4 Wageningen University, Netherlands 5 University College Dublin, Ireland 6 UCD Geary Institute for Public Policy, Ireland 7 Childrens' Health Ireland at Temple Street, Ireland 8 Royal College of Surgeons, Dublin, Ireland 9 International English School, Sweden 10 Ellinogermaniki Agogi, Greece 11 Ekpaideftiria N. Mpakogianni, Greece 12 COSMOTE, Greece 13 International Hellenic University, Greece 14 Mysphera, Spain has been attributed to the complex nature of the disease [4], implying that effective interventions must be evidencebased, adapted to the local context and address multiple obesogenic factors of the environment [3], [5], even on a local neighborhood level [6].…”

Section: Introductionmentioning

confidence: 99%

BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

Diou¹,

Sarafis²,

Papapanagiotou³

et al. 2021

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Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram. eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.

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“…To safeguard the healthy growth of school-age children, it is of great significance to explore the space and laws of their behaviors, with full consideration of their mental needs and physiological scale. The proliferation of video surveillance system makes it possible to recognize, analyze, and summarize the behavior features of school-age children, with the aid of advanced image processing technology [5][6][7][8][9]. Some scholars have theoretically summarized the behavior features of school-age children [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Recognition and Analysis of Behavior Features of School-Age Children Based on Video Image Processing

Zhen¹

2020

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School-age children have vastly different behavior features from adults. Most of the relevant studies are theoretical summaries of behavior features of these children, failing to detect the behaviors or recognize the behavior features in an accurate manner. To solve the problem, this paper puts forward a novel method to recognize the behavior features of school-age children through video image processing. Firstly, the authors designed a method to extract static behavior features of school-age children from surveillance video images. Next, the behavior features of school-age children were extracted by optical flow method. On this basis, a dual-network flow neural network (DNFNN) was designed, in which the time flow network processes the dense optical flow of multiple continuous frames of the surveillance video, while the spatial flow network treats the region of interest (ROI) in the static frame from the video. After that, the workflow of the DNFNN was introduced in details. Experimental results fully demonstrate the effectiveness of the proposed network. The research findings provide a reference for the application of video image processing to behavior recognition in other fields.

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Developing a Novel Citizen-Scientist Smartphone App for Collecting Behavioral and Affective Data from Children Populations

Cited by 12 publications

References 9 publications

Novel e-Health Applications for the Management of Cardiometabolic Risk Factors in Children and Adolescents in Greece

Novel e-Health Applications for the Management of Cardiometabolic Risk Factors in Children and Adolescents in Greece

BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

Recognition and Analysis of Behavior Features of School-Age Children Based on Video Image Processing

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