Camera-based systems for smart homes offer a comprehensive and unobtrusive approach to monitoring the well-being of elderly individuals within the comfort of their homes. By leveraging computer vision and artificial intelligence, these unobtrusive cameras can monitor daily activities, detect anomalies, and even provide real-time insights into the well-being of seniors. Overall, the integration of AI with vision-based technologies offers unparalleled opportunities for continuous health monitoring and improved quality of life for seniors.
We propose an open special session focusing on the transformative field of “Advances in Vision-Based Monitoring of the Elderly for a Smart Home Environment.” This dedicated session will provide a platform for in-depth exploration and discussion of the latest innovations and challenges, and bring together experts from healthcare, AI, gerontology, and engineering to share insights, best practices, and collaborative approaches in the development and implementation of smart home solutions tailored for the elderly.
Discussions within the session will encompass practical applications of vision-based monitoring, emphasizing early detection of health issues, preventative care, and overall improvements in the quality of life for elderly individuals. Key topics to be discussed include short/long term behavioural analysis (e.g., eating behavioural analysis), action quality assessment, activity recognition, temporal action detection/segmentation, facial expression understanding, vital sign monitoring using vision- based systems. We have opted to omit fall detection from our focus, given the extensive existing research on the subject. This decision is based on the recognition that fall detection merits its own dedicated session due to the substantial volume of available research and advancements in this specific area.


Robert B. Fisher (School of Informatics, The University of Edinburgh),
Muhammad Ahmed Raza (School of Informatics, The University of Edinburgh),

Submission format:

This Special Session welcomes both full length papers (12 pages plus up to 2 pages of references) and abstracts (3 pages including references).

Deadline: All deadlines, including submission deadline and review timeline are the same as the main conference. Please follow this link to see all the Important Dates.

If you have any questions regarding this Special Session, please contact the organiser.

Click here to submit your papers on CMT.