Doctoral thesis
Open access

Enhancing wellbeing using artificial intelligence techniques

Defense date2019-12-19

The landscape of technology is rapidly evolving, and its pace of growth is not slowing down. During the last decades, and especially after the mass adoption of the internet, new technological advances have revolutionized every aspect of human life. We are living in the ubiquitous computing era, where connected devices form the internet of things and produce data faster than we can logically process. The goal of this thesis is to propose new algorithms, methodologies, and applications that can be used as components in health and wellbeing systems that support healthy aging, enhance human-machine interactions, and support postoperative rehabilitation with the use of machine learning techniques.

  • Abnormality detection
  • Active assisted living
  • Activity recognition
  • Ambient assisted living
  • Bluetooth
  • Bluetooth low energy
  • Deep learning
  • EHealth
  • Feature extraction
  • Health informatics
  • Indoor localization
  • Indoor positioning
  • Machine learning
  • MHealth
  • Mobile applications
  • Mobile devices
  • Neural networks
  • Pattern recognition
  • Postoperative rehabilitation
  • Room-level accuracy
  • RSSI
  • Senior citizens
  • Smart devices
  • Smartphones
  • Smartwatches
  • Stress detection
  • Survey
  • User requirements
  • Wearable computers
  • Wearable sensors
Citation (ISO format)
KYRITSIS, Athanasios. Enhancing wellbeing using artificial intelligence techniques. 2019. doi: 10.13097/archive-ouverte/unige:130751
Main files (1)

Technical informations

Creation02/12/2020 1:58:00 PM
First validation02/12/2020 1:58:00 PM
Update time03/15/2023 9:08:37 PM
Status update03/15/2023 9:08:36 PM
Last indexation01/29/2024 10:06:38 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack