Niall Twomey

TL;DR: An overview of this rapidly growing body of work on sensing systems in the home, as well as the implications for machine learning are presented, with an aim of uncovering the gap between the state of the art and the broad needs of healthcare services in ambient assisted living.

TL;DR: This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers, particularly focused on long-term activity recognition in real-world settings.

TL;DR: The home possesses unique characteristics that must be considered in order to develop effective smart home systems that are adopted in the real world and broadly underpinned by shared goals of sustainable development, inclusive user engagement and improved service delivery.

TL;DR: The SPHERE challenge is an activity recognition competition where predictions are made from video, accelerometer and environmental sensors and prizes will be awarded to the top three entrants.

TL;DR: The hypothesis is that learning patterns based on combinations of sensors will be more powerful than single sensors alone and this work shows that through the application of signal processing and information-theoretic techniques it can achieve classification performance better than that of the exhaustive approach, whilst only incurring a small cost in terms of computational resources.