920 citations · 1k across the 30 of their papers we have counts for
31 papers
Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity
Kexin Fan, Alexander Capstick, Ramin Nilforooshan +1
Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD), as they can lead to severe complications if not detected and…
Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model
Jin Cui, Alexander Capstick, Payam Barnaghi +1
In the analysis of remote healthcare monitoring data, time series representation learning offers substantial value in uncovering deeper patterns of patient behavior, especially giv…
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia
Jin Cui, Alexander Capstick, Payam Barnaghi +1
In remote healthcare monitoring, time series representation learning reveals critical patient behavior patterns from high-frequency data. This study analyzes home activity data fro…
Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment
Maria R. Lima, Alexander Capstick, Fatemeh Geranmayeh +4
Timely and accurate assessment of cognitive impairment remains a major unmet need. Speech biomarkers offer a scalable, non-invasive, cost-effective solution for automated screening…
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling
Alexander Capstick, Rahul G. Krishnan, Payam Barnaghi
Large language models (LLMs) acquire a breadth of information across various domains. However, their computational complexity, cost, and lack of transparency often hinder their dir…
Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction
Yu Chen, Tianyu Cui, Alexander Capstick +2
In Explainable AI, rule extraction translates model knowledge into logical rules, such as IF-THEN statements, crucial for understanding patterns learned by black-box models. This c…
Representation Learning of Daily Movement Data Using Text Encoders
Alexander Capstick, Tianyu Cui, Yu Chen +1
Time-series representation learning is a key area of research for remote healthcare monitoring applications. In this work, we focus on a dataset of recordings of in-home activity f…
Low-Energy On-Device Personalization for MCUs
Yushan Huang, Ranya Aloufi, Xavier Cadet +3
Microcontroller Units (MCUs) are ideal platforms for edge applications due to their low cost and energy consumption, and are widely used in various applications, including personal…
Interpreting Differentiable Latent States for Healthcare Time-series Data
Yu Chen, Nivedita Bijlani, Samaneh Kouchaki +1
Machine learning enables extracting clinical insights from large temporal datasets. The applications of such machine learning models include identifying disease patterns and predic…
A Markov Chain Model for Identifying Changes in Daily Activity Patterns of People Living with Dementia
Nan Fletcher-Lloyd, Alina-Irina Serban, Magdalena Kolanko +5
Malnutrition and dehydration are strongly associated with increased cognitive and functional decline in people living with dementia (PLWD), as well as an increased rate of hospital…
Information Theory Inspired Pattern Analysis for Time-series Data
Yushan Huang, Yuchen Zhao, Alexander Capstick +3
Current methods for pattern analysis in time series mainly rely on statistical features or probabilistic learning and inference methods to identify patterns and trends in the data.…
Training Neural Networks on Data Sources with Unknown Reliability
Alexander Capstick, Francesca Palermo, Tianyu Cui +1
When data is generated by multiple sources, conventional training methods update models assuming equal reliability for each source and do not consider their individual data quality…
Using Entropy Measures for Monitoring the Evolution of Activity Patterns
Yushan Huang, Yuchen Zhao, Hamed Haddadi +1
In this work, we apply information theory inspired methods to quantify changes in daily activity patterns. We use in-home movement monitoring data and show how they can help indica…
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Francesca Palermo, Honglin Li, Alexander Capstick +6
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individu…
Multimodal Federated Learning on IoT Data
Yuchen Zhao, Payam Barnaghi, Hamed Haddadi
Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world…
Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia
Roonak Rezvani, Samaneh Kouchaki, Ramin Nilforooshan +2
Interpreting the environmental, behavioural and psychological data from in-home sensory observations and measurements can provide valuable insights into the health and well-being o…
An Intelligent Bed Sensor System for Non-Contact Respiratory Rate Monitoring
Qingju Liu, Mark Kenny, Ramin Nilforooshan +1
We present an IoT-based intelligent bed sensor system that collects and analyses respiration-associated signals for unobtrusive monitoring in the home, hospitals and care units. A…
A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare Data
Narges Pourshahrokhi, Samaneh Kouchaki, Kord M. Kober +2
Missing values exist in nearly all clinical studies because data for a variable or question are not collected or not available. Inadequate handling of missing values can lead to bi…
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia
Honglin Li, Roonak Rezvani, Magdalena Anita Kolanko +5
Behavioural symptoms and urinary tract infections (UTI) are among the most common problems faced by people with dementia. One of the key challenges in the management of these condi…
Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots
Maitreyee Wairagkar, Maria R Lima, Daniel Bazo +10
We introduce the conceptual formulation, design, fabrication, control and commercial translation with IoT connection of a hybrid-face social robot and validation of human emotional…
Deep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia
Honglin Li, Magdalena Anita Kolanko, Shirin Enshaeifar +13
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in lon…
Semi-supervised Federated Learning for Activity Recognition
Yuchen Zhao, Hanyang Liu, Honglin Li +2
Training deep learning models on in-home IoT sensory data is commonly used to recognise human activities. Recently, federated learning systems that use edge devices as clients to s…
Verifying the Causes of Adversarial Examples
Honglin Li, Yifei Fan, Frieder Ganz +2
The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs i…
Continual Learning Using Multi-view Task Conditional Neural Networks
Honglin Li, Payam Barnaghi, Shirin Enshaeifar +1
Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known…