Human activity recognition systems are developed as part of a framework to enable
continuous monitoring of human behaviours in the area of ambient assisted living, sports …

Today, patients are demanding a newer and more sophisticated health care system, one
that is more personalized and matches the speed of modern life. For the latency and energy …

This department provides an overview the progress the authors have made to the emerging
area of embedded and mobile forms of on-device deep learning. Their work addresses two …

We focus on the problem of wearer's action recognition in first person aka egocentric videos.
This problem is more challenging than third person activity recognition due to unavailability …

Integral to the adoption and uptake of AI systems in real-world settings is the ability for
people to make sense of and evaluate such systems, a growing area of development and …

One of the main challenges of social interaction in virtual reality settings is that head-
mounted displays occlude a large portion of the face, blocking facial expressions and …

Wearable devices with built-in cameras present interesting opportunities for users to capture
various aspects of their daily life and are potentially also useful in supporting users with low …

This paper introduces a vision-based deep learning approach that enables the detection
and recognition of occupants' activities within building spaces. The data can feed into …

Deep learning techniques achieve state-of-the-art performance on many computer vision
related tasks, eg large-scale object recognition. In this paper we show that recognition …

Visual lifelogging consists of acquiring images that capture the daily experiences of the user
by wearing a camera over a long period of time. The pictures taken offer considerable …