The prevalence of wearable sensing devices and smartphones is resulting in a multitude of physiological data being collected, for example heart rate, gait and eye movement. Driven by applications in health and behavioural monitoring, as well as affective computing, there is a growing demand for computational models that are able to accurately predict multimodal features in a variety of contexts. While machine learning models excel at identifying features in physiological signals, they lack reliability guarantees and need to be adapted to the user. This talk will give an overview of modelling and personalisation techniques developed as part of the AffecTech project (http://www.cs.ox.ac.uk/projects/AFFECTech/index.html) and their applications in the context of biometric security and emotion recognition. Future challenges in this important field will also be discussed.