Remote Monitoring of Human’s Activities and Health

Case Study Team


Id Name Institution (type*) Related WG Role in the team
1 Sabri Pllana Linnaeus University, SE (A)  WG2 Coordinator
2 Dragan Sotjanovic University of Nis, RS  (A) WG1 Vice-Coordinator

Team members

Id Name Institution (type*) Related WG
1  Siegfried Benkner  University of Vienna, AT  (A)  WG2
2  Daniel Grzonka  Cracow University of Technology, PL (A)  WG1
3 Farhoud Hosseinpour University of Turku, FI  (A)  WG1
4  Agnieszka Jakóbik  Cracow University of Technology, PL (A)  WG1
5 Helen Karatza  University of Thessaloniki, GR (A) WG2
6 Joanna Kolodziej  NASK , PL (I)  WG1
7 Mateusz Krzyszton NASK, PL (I) WG1
8 Zuzana Kominkova-Oplatkova Tomas Bata University of Zlin , CZ  (A)  WG4
9 Francesco Masulli University of Genoa, IT (A)  WG4
10  Ilias Mavridis  University of Thessaloniki, GR (A)  WG2
11 Jose Manuel Molina Lopez Universidad Carlos III de Madrid, ES (A) WG2
12 Michal Marks Warsaw University of Technology (A), NASK, PL (I) WG1
13 Ewa Niewiadomska-Szynkiewicz Warsaw University of Technology, PL (A) WG1
14  Ana Respicio University of Lisbon,  PT  (A)  WG4
15 Andrzej Sikora Warsaw University of Technology (A), NASK, PL (I) WG1
16 George Suciu  BEIA Consult International, RO (A)  WG1
 17 Salvatore Vitabile  University of Palermo, IT (A), MIRC s.r.l. (I)  WG3
18 Ioan Salomie Technical University of Cluj-Napoca, RO (A) WG1

*A-academia, I-industry

Addressed Problem(-s)

The Topics we focus on

  • Remote monitoring of health conditions of the patients – Body Area Networks (small area sensor networks)
  • Remote monitoring of the human’s activities – sensor-based monitoring systems, support in emergency situations, situated awareness
  • Cloud support systems for processing and analytics of the data generated by the above sensor-based monitoring systems

Main Problems related to the topics

Individual monitoring

  • Security and privacy
  • Data ownership
  • Individual (personal) data analytics
  • Data fusion
  • Edge-Cloud trade-off for processing/analysis sensor data

Crowd modelling and monitoring

  • Collective data analytics
  • Data fusion
  • Security and privacy
  • Data ownership

Existing Solution(-s) (Models, Tools)

  • Heterogeneous data fusion
  • Deep learning in e-health
  • Virtual layer for medical data abstraction/anonymization
  • open source Cloud IoT framework; sensors and actuators (
  • Containers technology
  • Existing data:,
  • Situated MAS (crowd or individual modeling)
  • Private Cloud clusters for medical data storage and analytics (Comarch, Philips)
  • FIWARE ( processing sensor data

Proposed Solution(-s) (Models, Tools)

Data mining

  • High-performance data analysis
  • Context-aware data fusion & processing 
  • Incremental clustering of data streams
  • Adaptive [edge vs cloud] processing of sensor data

Methods and models

  • Artificial Intelligence models and methods
    • Artificial Neural Networks
    • Fuzzy logic
    • Machine learning
    • Bioinspired methods
  • Blockchain
  • Intelligent agents
  • Modelling, simulation and optimisation

Tools, devices and platforms

  • Sensors & actuators
  • IoT platform 
  • Cloud, Fog/Edge computing
  • Smart dust computing
  • Wearable computing
  • Simulators

Practical Scenarios (-s)

  • Remote monitoring of the heart condition of the taxi drivers in Cracow (Comarch)
  • Remonte monitoring of the chemical engineers in the dangerous environments (ABB)
  • Smart Phone Ad hoc Network (SPAN) for e-health application (NASK)
  • Machine learning for daily living activities of people with dementia

Supplementary Material