As the elderly population increases, automatically supporting their life is becoming a priority in the research community. To be able to provide such support, however, the focus of measuring ageing and health should shift from the current symptom-based approaches to personalised situation-aware approaches. As there are currently no established roadmaps on how to measure healthy ageing, we are collaborating with the Gerontology Department at the University of Zürich (UZH) on methods for measuring various cognitive and physical properties based on sensor, video, audio and textual data. Through Prof. Mike Martin, who is the chairman of the Center for Gerontology in UZH, we are involved in the effort of the World Health Organization to elaborate the concept of healthy ageing and establish a dynamic system model of health. This involves the semantic analysis of the health dynamics within and across persons and time.
- Teodor Stoev, Aandrea Ferrario, Burcu Demiray, Minxia Luo, Mike Martin, Kristina Yordanova. Coping with Imbalanced Data in the Automated Detection of Reminiscence from Everyday Life Conversations of Older Adults. IEEE Access, 2021. [full text]
- Andrea Ferrario, Burcu Demiray, Kristina Yordanova, Minxia Luo, Mike Martin. Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning. Journal of Medical Internet Research. 2020. 22(9):e19133 [full text]
- Kristina Y. Yordanova, Burcu Demiray, Matthias R. Mehl, Mike Martin. Automatic Detection of Everyday Social Behaviours and Environments from Verbatim Transcripts of Daily Conversations. In Proceedings of IEEE International Conference on Pervasive Computing and Communications. Kyoto, Japan. 2019. [full text]