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TRICARE

Monitoring of daily living activities for elderlies

The TRICARE project is concerned with the use of non-intrusive sensors to monitor elderly’s activities of daily living (ADL), thus in order to detect eventual behavior anomalies while preserving their privacy.

Objectives

TriCARE is a CWALITY project with a business valorization driven by AnB, a Belgian-based company developing alarm systems. This company is looking for extending their actual alarm systems with software-based added value services. One of these services is based on the monitoring of single living elderly’s ADLs. This is the objective of the project TriCARE. This monitoring service will be designed and developed under the following constraints:

  • Right to privacy: The system will alert family members about potential anomaly behaviour of their elderly relative so that they can take contact with him/her to verify the situation. It is not considered to be a reporting system of very precise daily life actions, which may alter the elderly’s privacy.
  • Use of simple, basic sensors: Monitoring solutions relying to intrusive sensors like camera have failed to get accepted by elderly who feel being daily supervised. Therefore, very simple non-intrusive sensors like infrared sensors, door contact sensors, and pressure sensors will be considered and eventually combined with energy and water meters.
  • Low cost solution: ADL monitoring systems have the potential to predict behavior anomalies and therefore reduce socio-security costs associated with post-treatment of health problems linked to the behavior changes. However, until now there are not any incentives to motivate the adoption of those solutions. The cost is completely covered by either the elderly or his/her family. Therefore, the monitoring solution to be developed via TriCARE need to be affordable to ensure a viable business.

Project results

Based on criteria defined by geriatricians, CETIC will design and implement a generic model-based component that recognizes ADL from sensor time-series of sensor events and detect ADL anomalies, thus by relying on machine learning techniques. Our partner company will then conduct a pilot test campaign in different residence of elderly persons in order to validate the concept in real life scenario.

Added value

The generic component to be developed by CETIC will be made free and open source so that communities of multi-disciplinary developers (IT, Healthcare, families) may extend this component to take into account more psychological, medical and social scenarios. This component will serve as a basis for future research projects and also for integration in other hardware and software solutions.

Partners