Facilitating health and wellbeing by developing systems for early recognition of urinary tract infections - Feather
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Informatics
Abstract
Urinary tract infections (UTI) are one of the most common types of infection, impacting more than 92 million people worldwide. When diagnosed early, UTI's can readily be resolved with antibiotics. However, left untreated, it can lead to urosepsis (>1.6 million deaths), kidney damage and ultimately death.
Identifying the early symptoms of a UTI can be challenging, symptoms are different in younger and older people and people with underlying health conditions. In addition, there is no one symptom but a collection of symptoms that indicate a person has a UTI. These include changes to urine: colour, smell, frequency; incontinence, pain, confusion, agitation delirium, temperature, shaking and changes in sleep. This concoction of symptoms is what makes identifying the signs of a UTI difficult for the person concerned and/or their carers.
This project will gather data about the activities of an individual in their home on a continuous basis. By having data on activities of daily living, it is possible to identify changes. The Feather platform will combine these data points to recognise the emergence of a UTI. This is in fact quite difficult for an individual themselves or a carer to see. Thus, the Feather project will be able to raise an alert for investigation of a UTI, before a person could do it themselves, enabling early intervention, to get appropriate treatment quickly. For example, the following can all be indicators of behaviours that are impacted by the symptoms of UTI's, patterns for activities of daily behaviour, e.g. kettle use, change in walking pace, cognitive function through interaction with an intelligent agent, e.g. an embodied robot.
The Feather project could enable a GP to have the time to wait for lab results to urine tests to ensure appropriate prescription of antibiotics, reducing the cost to the NHS of prescriptions and improving outcomes for the patient. Early diagnosis should reduce the number of patients presenting at A&E and reduce the number of cases developing to urosepsis, kidney failure and ultimately death.
Working with stakeholders, we will co-design, co-implement and co-evaluate the Feature project.
Identifying the early symptoms of a UTI can be challenging, symptoms are different in younger and older people and people with underlying health conditions. In addition, there is no one symptom but a collection of symptoms that indicate a person has a UTI. These include changes to urine: colour, smell, frequency; incontinence, pain, confusion, agitation delirium, temperature, shaking and changes in sleep. This concoction of symptoms is what makes identifying the signs of a UTI difficult for the person concerned and/or their carers.
This project will gather data about the activities of an individual in their home on a continuous basis. By having data on activities of daily living, it is possible to identify changes. The Feather platform will combine these data points to recognise the emergence of a UTI. This is in fact quite difficult for an individual themselves or a carer to see. Thus, the Feather project will be able to raise an alert for investigation of a UTI, before a person could do it themselves, enabling early intervention, to get appropriate treatment quickly. For example, the following can all be indicators of behaviours that are impacted by the symptoms of UTI's, patterns for activities of daily behaviour, e.g. kettle use, change in walking pace, cognitive function through interaction with an intelligent agent, e.g. an embodied robot.
The Feather project could enable a GP to have the time to wait for lab results to urine tests to ensure appropriate prescription of antibiotics, reducing the cost to the NHS of prescriptions and improving outcomes for the patient. Early diagnosis should reduce the number of patients presenting at A&E and reduce the number of cases developing to urosepsis, kidney failure and ultimately death.
Working with stakeholders, we will co-design, co-implement and co-evaluate the Feature project.
Publications
Lister E
(2024)
An Open-Source Neurodynamic Model of the Bladder
McConnell-Trevillion A
(2024)
Low Frequency Tibial Neuromodulation Increases Voiding Activity - A Computational Model
Ju W
(2025)
Smart Wearable TENS Device for Home-based Overactive Bladder Management.
in IEEE transactions on biomedical circuits and systems
| Description | Early stage research - but we have demonstrated several methodologies that enables continuous gait moniotring as well as human robot interaction |
| Exploitation Route | Long term trials are planned for the coming year |
| Sectors | Healthcare |
| Title | lower urinary tract simulator |
| Description | This repository contains a Python-based model that simulates the dynamics of the bladder, sphincter, and kidney, using normalised neural signals to predict pressure and volume of the bladder. For more detailed mathematical explanations, please refer to the paper: An Open-Source Neurodynamic Model of the Bladder. DOI: 10.1101/2024.11.21.624716 |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Stochastic Kidney Function: Reflects natural fluctuations in urine production throughout the day, incorporating randomness to account for biological variability. Neural Input Modeling: : Features a built-in parameter-based model to simulate neural signals from the brain. This allows for simple modeling of neurological conditions and their effects on the lower urinary tract, as well as the use of external neural models to stimulate the detrusor and sphincter muscles. |
| URL | https://github.com/MoveR-Digital-Health-and-Care-Hub/lower-urinary-tract-sim |
| Description | Blackwood Homes |
| Organisation | Blackwood |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | We have worked with Blackwood Homes to undertake requirements gathering and recruit participants for evaluations of early prototypes of our system. |
| Collaborator Contribution | Blackwood Homes has worked with us to provide data for requirements gathering and recruit participants for evaluations of early prototypes of our system. |
| Impact | Nault, E, Baillie, L & Bettosi, C 2024, Designing a Socially Assistive Robot for the Early Identification of Urinary Tract Infections. in 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). IEEE, pp. 1170-1176, 33rd IEEE International Conference on Robot and Human Interactive Communication 2024, Pasadena, California, United States, 26/08/24. https://doi.org/10.1109/ro- man60168.2024.10731395 |
| Start Year | 2024 |
| Description | Leuchie House Charity Technology Showcase and Fund Raiser |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | The Leuchie House Charity Technology Showcase and Fundraiser event was to raise awareness of the charitys technology use and how they enganged with technology research, such as this project, that would benefit their patients and carers. They were wishing to raise that awareness with their Patron HRH Princess Anne and their donors. The FEATHER team demoed some early scenarios of use on a robot and answered questions about the research teams engagement with Leuchie House. |
| Year(s) Of Engagement Activity | 2023 |