920 citations · 1k across the 30 of their papers we have counts for
21 papers · 1 filter
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…
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…
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…
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…
Continual Learning Using Bayesian Neural Networks
HongLin Li, Payam Barnaghi, Shirin Enshaeifar +1
Continual learning models allow to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios in which the models are trained using…
Continual Learning in Deep Neural Network by Using a Kalman Optimiser
Honglin Li, Shirin Enshaeifar, Frieder Ganz +1
Learning and adapting to new distributions or learning new tasks sequentially without forgetting the previously learned knowledge is a challenging phenomenon in continual learning…
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Honglin Li, Frieder Ganz, Shirin Enshaeifar +1
Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment. Learning new tasks without forgetting the prev…
Machine learning for Internet of Things data analysis: A survey
Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain +3
Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurem…
Segment Parameter Labelling in MCMC Mean-Shift Change Detection
Alireza Ahrabian, Shirin Enshaeifar, Clive Cheong-Took +1
This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the paramete…