Integrated Support System for the Elderly, for people with health problems and lonely workers by Utilizing Wearable Devices and Machine Learning Algorithms



Code of Act (Project): ΗΠ1ΑΒ-00127

Implementation Period: 25/09/2018 – 13/05/2022

Budget: 146.127,50 €

Scientifically Responsible: Prof. Chrysostomos Stylios

TrackMyHealth proposes an integrated system of monitoring and support of individuals (elderly people, lonely workers) using wearables devices and machine learning algorithms.

The TrackMyHealth project proposes an integrated monitoring and support system for individuals (elderly, lonely workers) utilising wearables and machine learning algorithms.

The aim of the project is to create an IoT network for monitoring the health of individuals through continuous measurement of various characteristics, recording, and analysis for the detection of critical situations in real-time. The system consists of a wearable device, a mobile application, as well as a network platform. The measurements are performed using sensors built into the wearable device while they are stored and processed locally on smartphones available to various users. They are then transferred to an online platform where they are processed through a decision support system that assesses the health levels of each user in order to detect potentially dangerous health conditions (e.g., abrupt change in heart rate at rest). In addition, there is the possibility of accessing the online application for monitoring the health history of various individuals and the automatic informing of those directly concerned (e.g., relatives, supervising doctors, employers, etc.) in case of emergency (e.g., fall elderly person, etc.).

The system is an easy-to-use solution for users as it consists of a wearable device (wristwatch) that does not interrupt/burden the flow of activities of each individual and an application developed for smartphones (mobile app) that does not require specialised knowledge for its operation. This project uses a device that has a heart rate sensor as well as motion detection sensors (accelerometer, gyroscope).

Overall, the aim of this project can be summarised as:

  • Involvement in health monitoring and personal fitness improvement (self-monitoring)
  • Evaluation and feedback (from the system) of basic health levels based on reliable machine learning and decision making algorithms
  • Prevention of dangerous health situations and promotion of well-being
  • Continuous remote monitoring by supervisors (responsible for each user)
  • Informing (sending notifications) responsible in cases of immediate danger for intervention/sending help to the person when necessary.


The Program is co-financed by the European Union And from the National Resources of the participating States

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