Safe Health Elderly Monitoring

Project duration: June 2021 to June 2023

The Safe Health Elderly Monitoring (SAFHE) project aims to develop a wearable device, with several biological sensors, clinically relevant and user-friendly, combined with an environment control station. Through artificial intelligence (AI) based on machine learning techniques, it will enable the remote detection of signs of pathologic conditions and remote monitoring of the elderly from their home or nursing home.

This system contributes to meeting the political priority established in the European Countries by promoting elderly people’s deinstitutionalization and the implementation of measures that reinforce the transition from institutional services to services based in the community. 


Metro 2
Metro 2
Metro 2
Metro 2

More about the project:

By remotely monitoring the elders during daily activities in their home environment, the SAFHE system would be a solution to improve elderly people’s safety and cardiorespiratory health, but also to be used in stringent conditions, such as the pandemic created by COVID-19. The technological evolution associated with the progress in the communication industry made possible the integration of monitorization devices in a real context enabling the development of solutions to process and integrate signals of different sources through AI.

This project will provide access to a set of data from an easy to use wrist like wearable with integrated clinically relevant sensors and an ECG wearable. These devices will be combined with an environmental monitoring station hub that will serve as both environmental sensing and gateway between the wearable and the cloud structure, as well as local processing and IoT integration. 

Project Goals:

  • Assembly of a multi-sensory wearable hardware device to access cardiorespiratory and physical activity related variables adapted to the elderly.
  • Assembly of an environmental control station Hub that can store and upload the data to a cloud secure server, from the wearable data adapted to the elderly. The station will be able to monitor air quality.
  • Development of an AI solution able to configure the measurement parameters in real-time to optimize the data quality and identify the pathological signs.
  • Development of a user interface dashboard for healthcare professionals or caregivers to monitor daily clinical and environmental parameters from the users.
  • Implementation of pilot studies involving the proposed system in real conditions.

   Co-funded by: