Daniel Leightley

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—The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the(More)
This paper aims to investigate whether micro-facial movement sequences can be distinguished from neutral face sequences. As a micro-facial movement tends to be very quick and subtle, classifying when a movement occurs compared to the face without movement can be a challenging computer vision problem. Using local binary patterns on three orthogonal planes(More)
We present the development of a virtual trainer for use by physiotherapists and patients in exercise based physiotherapy programmes. It allows a therapist to tailor exercise requirements to the specific needs and challenges of individual patients. Patients can select different programmes and follow a coach avatar to perform recorded exercises based on their(More)
Recent works on human action recognition have focused on representing and classifying articulated body motion. These methods require a detailed knowledge of the action composition both in the spatial and temporal domains, which is a difficult task, most notably under real-time conditions. As such, there has been a recent shift towards the exemplar paradigm(More)
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