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.