Risk Prevention of Spreading Emerging Infectious Diseases Using a HybridCrowdsensing Paradigm, Optical Sensors, and Smartphone

The risk of spreading diseases within (ad-hoc)crowds and the need to pervasively screen asymptomatic individuals to protect the population against emerging infectious diseases, request permanentcrowd surveillance., particularly in high-risk regions. Thecase of Ebola epidemic in West Africa in recent years has shown the need for pervasive screening. The trend today in diseases surveillance is consisting of epidemiological data collection about emerging infectious diseases using social media, wearable sensors systems, or mobile applications and data analysis. This approach presents various limitations. This paper proposes a novel approach for diseases monitoring and risk prevention of spreading infectious diseases. The proposed approach, aiming at overcoming the limitation of existing disease surveillance approaches, combines the hybrid crowdsensing paradigm with sensing individuals’ bio-signals using optical sensors for monitoring any risks of spreading emerging infectious diseases in any (ad-hoc) crowds. A proof-of-concept has been performed using a drone armed with a cat s60 smartphone featuring a Forward Looking Infra-Red (FLIR) camera. According to the results of the conducted experiment, the concept has the potential to improve the conventional epidemiological data collection. The measurement is reliable, and the recorded data are valid. The measurement error rates are about 8%.

[1]  Anton Umek,et al.  Evaluation of Smartphone Inertial Sensor Performance for Cross-Platform Mobile Applications , 2016, Sensors.

[2]  Dutta Joy,et al.  AirSense: Opportunistic crowd-sensing based air quality monitoring system for smart city , 2016 .

[3]  Thierry Chateau,et al.  Pedestrian detection method using a multilayer laserscanner: Application in urban environment , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  L. Thévenaz,et al.  Simplex-coded BOTDA fiber sensor with 1 m spatial resolution over a 50 km range. , 2010, Optics Letters.

[5]  Stefano Chessa,et al.  Empowering mobile crowdsensing through social and ad hoc networking , 2016, IEEE Communications Magazine.

[6]  Demetrios Zeinalipour-Yazti,et al.  Crowdsourcing with Smartphones , 2012, IEEE Internet Computing.

[7]  Haiying Shen,et al.  A Survey of Mobile Crowdsensing Techniques: A Critical Component for the Internet of Things , 2016, ICCCN.

[8]  A. Frontera,et al.  Smart-watches: a potential challenger to the implantable loop recorder? , 2016, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[9]  Patrick Langdon,et al.  A Multimodal End-2-End Approach to Accessible Computing , 2015, Human–Computer Interaction Series.

[10]  Eirini Christaki.  New technologies in predicting, preventing and controlling emerging infectious diseases , 2015, Virulence.

[11]  Shu Liu,et al.  Shape-based Pedestrian Detection in Infrared Images , 2007, J. Inf. Sci. Eng..

[12]  Thierry Oscar Edoh,et al.  Using Information Technology for an Improved Pharmaceutical Care Delivery in Developing Countries. Study Case: Benin , 2011, Journal of Medical Systems.

[13]  Debopam Acharya,et al.  Contextual sensitivity of the ambient temperature sensor in Smartphones , 2015, 2015 7th International Conference on Communication Systems and Networks (COMSNETS).

[14]  Tieniu Tan,et al.  A Heuristic Deformable Pedestrian Detection Method , 2010, ACCV.

[15]  R. S. H. Istepanian,et al.  The potential of Internet of m-health Things “m-IoT” for non-invasive glucose level sensing , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  H WilliamsPatriciaA,et al.  Always connected: The security challenges of the healthcare Internet of Things , 2016 .

[17]  Yuguang Fang,et al.  Private Data Analytics on Biomedical Sensing Data via Distributed Computation , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[18]  Ben Y. Reis,et al.  Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project , 2008, PLoS medicine.

[19]  David J. Webb,et al.  Ultra Small Integrated Optical Fiber Sensing System , 2012, Sensors.

[20]  Tomaz Javornik,et al.  Smartphone with Augmented Gateway Functionality as Opportunistic WSN Gateway Device , 2014, Wirel. Pers. Commun..

[21]  Larry S. Davis,et al.  Probabilistic template based pedestrian detection in infrared videos , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[22]  Eduardo Casilari-Pérez,et al.  On the Capability of Smartphones to Perform as Communication Gateways in Medical Wireless Personal Area Networks , 2014, Sensors.

[23]  Nicolas P. Terry,et al.  Will the Internet of Things Disrupt Healthcare? , 2016 .

[24]  Giancarlo Fortino,et al.  Enabling IoT interoperability through opportunistic smartphone-based mobile gateways , 2017, J. Netw. Comput. Appl..

[25]  Thierry Chateau,et al.  Détection de piétons à l'aide d'un capteur laser quatre nappes embarqué Pedestrian Detection using a Multilayer Laserscanner , 2008 .