activity
20182021
most citedSpatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

11 citations · 16 across the 4 of their papers we have counts for

collaborators

12 papers

eess.SY2021

Optimizing the Level of Challenge in Stroke Rehabilitation using Iterative Learning Control: a Simulation

Sandra-Carina Noble, Tomas Ward, John V. Ringwood

The level of challenge in stroke rehabilitation has to be carefully chosen to keep the patient engaged and motivated while not frustrating them. This paper presents a simulation wh…

cs.LG20211 cited

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Eoin Brophy, Maarten De Vos, Geraldine Boylan +1

Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare sy…

cs.CV20202 cited

IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report

Qi She, Fan Feng, Qi Liu +33

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top finalists (out of over~

cs.CV20202 cited

A Neuro-AI Interface for Evaluating Generative Adversarial Networks

Zhengwei Wang, Qi She, Alan F. Smeaton +2

Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. However, eva…

eess.SP2020

Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications

Eoin Brophy, Willie Muehlhausen, Alan F. Smeaton +1

Wrist-worn smart devices are providing increased insights into human health, behaviour and performance through sophisticated analytics. However, battery life, device cost and senso…

eess.SP2019

Synthesis of Realistic ECG using Generative Adversarial Networks

Anne Marie Delaney, Eoin Brophy, Tomas E. Ward

Access to medical data is highly restricted due to its sensitive nature, preventing communities from using this data for research or clinical training. Common methods of de-identif…