22 October 2024 13:00

Parallel session

AI for wearable health

By Cecilia Mascolo, Professor
Dept. of Computer Science and Technology, University of Cambridge, UK

Wearable devices are becoming pervasive in our lives, from smart watches measuring our physiology to wearables for the ear accompanying us in every run or virtual meeting. The monitoring of our health and fitness through sensors and wearables is the focus of much research in the community, however, despite advances in AI on wearable data, many challenges still remain before truly scalable, trustworthy and affordable wellness monitoring becomes a reality.  
In this talk I will discuss where commercial systems have gotten to today and highlight the open challenges that these technologies still face before they can be trusted health measurement proxies. Namely, the ability to work in the wild and to cope with the variability of uses; the trade offs that we need to consider with respect to the sensitivity of the data and the use of constrained on device resources; the uncertainty of the prediction over the data and the crucial need for robust, open AI frameworks to benchmark and assess performance in the context of critical applications such as health and fitness. I will mostly use examples from my team’s ongoing research on AI for health and fitness, on-device machine learning and “hearable” sensing to explore the current and future path of AI for wearable data. 

Cecilia Mascolo

Cecilia Mascolo is a Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is director of the Centre for Mobile, Wearable System and Augmented. She is also a Fellow of Jesus College Cambridge and the recipient of an ERC Advanced Research Grant.

She has been Deputy Head of Department between 2018 and 2021. Prior joining Cambridge in 2008, she was a faculty member in the Department of Computer Science at University College London. She holds a PhD from the University of Bologna.

Her research interests are in mobile systems and machine learning for mobile and wearable health. She has published in a number of top tier conferences and journals in the area and her investigator experience spans projects funded by Research Councils and industry.

She has served as steering, organizing and programme committee member of mobile and sensor systems, data science and machine learning conferences.