37 citations · 60 across the 8 of their papers we have counts for
13 papers
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…
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…