Remote Monitoring of Human’s Activities and Health

Case Study Team


Id Name Institution (type*) Contact e-mail 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*) Contact e-mail 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 Zuzana Kominkova-Oplatkova Tomas Bata University of Zlin , CZ  (A)  WG4
8 Francesco Masulli University of Genoa, IT (A)  WG4
9  Ilias Mavridis  University of Thessaloniki, GR (A)  WG2
10 Jose Manuel Molina Lopez Universidad Carlos III de Madrid, ES (A) WG2
11  Ana Respicio University of Lisbon,  PT  (A)  WG4
12 George Suciu  BEIA Consult International, RO (A)  WG1
13  Piotr Szuster  Cracow University of Technology, PL (A)  WG1
14 Jacek Tchorzewski  Cracow University of Technology, PL (A)  WG1
 15 Salvatore Vitabile  University of Palermo, IT (A)  WG3
16  Andrzej Wilczynski  Cracow University of Technology, PL (A)  WG1

*A-academia, I-industry

Adressed 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
  • Virtual layer for medical data abstraction/anonymization
  • open source Cloud IoT framework; sensors and actuators (
  • Containers technology
  • Existing data:,
  • Multi-agent systems (MAS)
  • Situated MAS (crowd or individual modeling)
  • Private Cloud clusters for medical data storage and analytics (Comarch, Philips)
  • Hadoop, G-Hadoop
  • FIWARE ( processing sensor data

Proposed Solution(-s) (Models, Tools)

  • Context-aware data fusion & processing model
  • Predictive fuzzy-neural networks
  • Sensors & actuators, IoT platform for data communication, data integration/fusion, context, decisions/ML, vertical application
  • Fog-edge computing
  • New methods for anomaly detection
  • Adaptive (edge vs cloud) processing of sensor data
  • High-performance data analytics (HPDA)
  • Incremental clustering of data streams
  • Innovative use of commercial electronic devices (smartphones & watches, bracelets, wearables,..) for remote monitoring
  • Radio-frequency identification (RFID)
  • Blockchain technology

Practical Scenarios (-s)


Supplementary Material