Security-Aware Fog-based Efficient Home Monitoring for Elders

Project name
SAFE-HOME – Security-Aware Fog-based Efficient Home Monitoring for Elders

CENTRO-01-0247-FEDER-072082 / LISBOA-01-0247-FEDER-072082

Main objective
Reinforce research, technological development, and innovation

Intervention area
Centro e Lisboa


  • LVS – UNIVERSAL, LDA, Project Leader
  • Instituto de Telecomunicações
  • Universidade de Aveiro
  • Altice Labs, S.A.

Approval date

Start date

End date

Total eligible costs
998.353,98 €

European Union funding
FEDER – CENTRO 2020: 563 038,44€ / FEDER – LISBOA 2020: 75 339,33 €

SAFE-HOME is an innovative international multi-disciplinary project, which aims at designing a monitoring system for elders to understand their activity level and with the ability to identify emergency situations. SAFE-HOME targets a non-wearable non-invasive and not dependent on users monitoring system, while preserving the privacy of its users. SAFE-HOME targets one of the 2030 sustainable development goals of the UN, namely “Good Health and Well-being”.

Main results
SAFE-HOME is foreseen to deliver innovative solutions for providing a non-invasive privacy/security-aware system for home-monitoring of elders, without jeopardizing their independence, autonomy, and without putting too much dependence on the users themselves. Some of SAFE-HOME main innovations can be listed as follows:

  • A new line of low-cost high-durability optical fiber sensors for measuring pressure and vibration on the floor, which can be used for monitoring users’ motion in enclosed areas;
  • An efficient privacy/security-aware delay-sensitive high-computational fog-cloud network enabling smart-home and eHealth application within buildings;
  • A security aware architecture, where the solution works on the security of the transmitted data, while preserving the privacy and identity of the users. The fog-cloud solution targets processing sensitive private data on user’s own fog, while sending anonymous data to the cloud, for privacy reasons;
  • The integration of highly non-invasive wearables to help with the monitoring of elderly with low dependence on the user, using advanced technologies, such as wireless charging to avoid the frequent need to charge;
  • A suite of artificial intelligence and learning algorithms, capable of classifying variations in perceived regular patterns, e.g., to identify different gait patterns, and different individuals, which can be used in home-security applications;
  • A full non-invasive home-monitoring system, tailored for elderly and vulnerable citizens, enabling them to live a fulfilling life, without jeopardizing their autonomy or lifestyle.

Co-financed by: