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Remote Monitoring of Human’s Activities and Health cHiPSet – CS6 Report

This report discusses the use of remote monitoring technologies for tracking human activities and health. It covers topics such as ultra-wide band body area networks, medical data processing and analysis, and case studies in remote health monitoring.

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Remote Monitoring of Human’s Activities and Health cHiPSet – CS6 Report

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  1. Remote Monitoring of Human’s Activities and Health cHiPSet –CS6Report SabriPllana On behalf of CS6 members March28 – 29, 2019, Vilnius (LT)

  2. Introduction Comarch e-CareBand, https://youtu.be/xRdpODAhTrU

  3. CS6 Outcome in Two Book* Chapters (I) Ultra Wide Band Body Area Networks: Design and integration with Computational Clouds Joanna Kołodziej, Daniel Grzonka, Adrian Widłak, Paweł Kisielewicz (II) Medical Data Processing and Analysis for Remote Health and Activities Monitoring Salvatore Vitabile, Michal Marks, Dragan Stojanovic, Sabri Pllana, Jose M. Molina, Mateusz Krzyszton, Andrzej Sikora, Andrzej Jarynowski, Farhoud Hosseinpour, Agnieszka Jakobik, Aleksandra Stojnev Ilic, Ana Respicio, Dorin Moldovan, Cristina Pop, and Ioan Salomie (*) High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet. Kolodziej, Joanna, Gonzalez-Velez, Horacio (Eds.), Springer 2019, https://www.springer.com/us/book/9783030162719

  4. (I) Ultra Wide Band Body Area Networks • Wireless Body Area Network (WBAN) • small network scale • long battery life • short range communications • security awareness • Ultra Wide Band Body Area Networks (UWB-BANs) • WBAN devices affect insignificantly human tissues • The chapter addresses • models, applications and research challenges • state-of-the art in the cloud-based support • security issues An example of Wireless BAN Cloud-based support to WBANs

  5. (II) Medical Data Processing and Analysis • Health monitoring systems • processing of data retrieved from smart phones, smart watches, smart bracelets, various sensors and wearable devices • The chapter addresses • data collection, fusion, ownership and privacy issues • models, technologies and solutions for medical data processing and analysis • big medical data analytics for remote health monitoring • research challenges and opportunities in medical data analytics • examples of case studies and practical solutions General architecture of a system for remote monitoring of people health and activities

  6. Example 1: Monitoring Patients With Chronic Heart Diseases • Contact person: Joanna Kolodziej • Research domain: mobility and e-health • sensors located on human body or clothes • monitoring patients with chronic heart diseases • experiment with 100 taxi drivers in Cracow • store data in COMARCH e-Care Center • Data • JHBD (JSON Header Binary Data) format used for ECG data • Software • OpenStack, C++, Java COMARCH Personal Medical Assistant COMARCH e-Care Remote Medical Care Centre

  7. Example 2: Smartphone Ad Hoc Network for e-Health • Contact person: Michal Marks • Distributed monitoring of human health • health monitoring during a trip • each trip participant has a smartphone and health sensors • monitor heart rate, body temperature,.. • trip participants are connected via Bluetooth • tourist guide is connected with trip participants, travel agency and rescue teams • SmartGroup@Net (SGN) mobile application enables direct communication via Bluetooth Low Energy (BLE) without using a cellular network • BLE: ~150m range, 1Mbps data rate A smartphone and Polar H7 heart rate sensor SGN application

  8. Example 3: ML-based Monitoring of Patients with Dementia • Contact person: IoanSalomie • Monitor activities of patients with dementia • use Raspberry Pi devices for monitoring activities (sleeping, feeding, mobility,..) • machine learning (random forest, k-means) implemented using Spark • detect anomalies in the pattern of daily activities • identify polypharmacy side-effects: health decline symptoms caused by the side effects of prescribed medications • use random forest for detecting the deviations of the daily life activities • use k-means for correlating deviations with the side-effects of drug-drug interactions A system for analyzing daily activities of patients with dementia using ML algorithms Detecting the side-effects of polypharmacybase on daily activities of patients with dementia

  9. Thank You For Your Attention • CS6 Contact Person • Sabri PLLANA (SE) • sabri.pllana@lnu.se CHIPSETChair Joanna KOLODZIEJ (PL) jokolodziej@pk.edu.pl CHIPSET Vice Chair Horacio GONZALEZ-VELEZ (IE) horacio@ncirl.ie Science Officer Ralph STUEBNER (COST) ralph.stuebner@cost.eu Web Presence http://www.cost.eu/COST_Actions/ict/IC1406 http://chipset-cost.eu/

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