16 citations · 80 across the 20 of their papers we have counts for
20 papers
Transfer Learning of RSSI to Improve Indoor Localisation Performance
Thanaphon Suwannaphong, Ryan McConville, Ian Craddock
With the growing demand for health monitoring systems, in-home localisation is essential for tracking patient conditions. The unique spatial characteristics of each house required…
Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices
Thanaphon Suwannaphong, Ferdian Jovan, Ian Craddock +1
This paper proposes small and efficient machine learning models (TinyML) for resource-constrained edge devices, specifically for on-device indoor localisation. Typical approaches f…
Multimodal Indoor Localisation in Parkinson's Disease for Detecting Medication Use: Observational Pilot Study in a Free-Living Setting
Ferdian Jovan, Catherine Morgan, Ryan McConville +3
Parkinson's disease (PD) is a slowly progressive, debilitating neurodegenerative disease which causes motor symptoms including gait dysfunction. Motor fluctuations are alterations…
Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detection
William Leeney, Ryan McConville
Graph Neural Networks (GNNs) have improved unsupervised community detection of clustered nodes due to their ability to encode the dual dimensionality of the connectivity and featur…
Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition
Armand K. Koupai, Mohammud J. Bocus, Raul Santos-Rodriguez +2
The pervasiveness of Wi-Fi signals provides significant opportunities for human sensing and activity recognition in fields such as healthcare. The sensors most commonly used for pa…
Ego-graph Replay based Continual Learning for Misinformation Engagement Prediction
Hongbo Bo, Ryan McConville, Jun Hong +1
Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinformation det…
Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers
Ferdian Jovan, Ryan McConville, Catherine Morgan +3
Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number…
Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes
Harry Emerson, Matthew Guy, Ryan McConville
The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically…
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
Dan Saattrup Nielsen, Ryan McConville
Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learnin…
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills
Taku Yamagata, Ryan McConville, Raul Santos-Rodriguez
A promising approach to improve the robustness and exploration in Reinforcement Learning is collecting human feedback and that way incorporating prior knowledge of the target envir…
OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors
Mohammud J. Bocus, Wenda Li, Shelly Vishwakarma +8
This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized R…
Photos Are All You Need for Reciprocal Recommendation in Online Dating
James Neve, Ryan McConville
Recommender Systems are algorithms that predict a user's preference for an item. Reciprocal Recommenders are a subset of recommender systems, where the items in question are people…
Social Influence Prediction with Train and Test Time Augmentation for Graph Neural Networks
Hongbo Bo, Ryan McConville, Jun Hong +1
Data augmentation has been widely used in machine learning for natural language processing and computer vision tasks to improve model performance. However, little research has stud…
Self-Supervised WiFi-Based Activity Recognition
Hok-Shing Lau, Ryan McConville, Mohammud J. Bocus +2
Traditional approaches to activity recognition involve the use of wearable sensors or cameras in order to recognise human activities. In this work, we extract fine-grained physical…
PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance
Alexander Hepburn, Valero Laparra, Jesús Malo +2
Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was us…
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback
Taku Yamagata, Raúl Santos-Rodríguez, Ryan McConville +1
Recent advances in both machine learning and Internet-of-Things have attracted attention to automatic Activity Recognition, where users wear a device with sensors and their outputs…
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
Ryan McConville, Raul Santos-Rodriguez, Robert J Piechocki +1
Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning…
Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance
Alexander Hepburn, Valero Laparra, Ryan McConville +1
In recent years there has been a growing interest in image generation through deep learning. While an important part of the evaluation of the generated images usually involves visu…
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks
Michal Kozlowski, Ryan McConville, Raul Santos-Rodriguez +1
As sensor networks for health monitoring become more prevalent, so will the need to control their usage and consumption of energy. This paper presents a method which leverages the…
Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable
Ryan McConville, Gareth Archer, Ian Craddock +4
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable…