Title: Continual learning in sensor-based human activity recognition

Abstract: Human activity recognition (HAR) is a key enabler for many applications in healthcare, factory automation, and smart home. It detects and predicts human behaviours or daily activities via a range of wearable sensors or ambient sensors embedded in an environment. As more and more HAR applications are deployed in the real-world environments, there is a pressing need for the ability of continually and incrementally learning new activities over time without retraining the HAR model. In this talk, we will present our recent progress in developing various continual learning techniques for HAR, including regularisation, generative rehearsal, and dynamic architecture techniques. We will summarise with what we have learnt from these projects and discuss future directions.

Bio: Dr. Juan Ye is a Reader in the School of Computer Science at the University of St Andrews. Her group’s research area centres around sensor-based human activity recognition, specialising in applied machine learning, continual learning and domain adaptation. Her research goal is to transform current sensor data analytics, equipping sensing systems with the capability to continuously learn new knowledge and automatically adapt to changes, and thus enabling long-term, large-scale, real-world deployment of applications with high impact.