Privacy-Preserved Human Motion Analysis for Healthcare Applications
Award Number
EP/W01212X/1Programme
Research GrantStatus / Stage
ActiveDates
18 September 2022 -17 September 2025
Duration (calculated)
02 years 11 monthsFunder(s)
EPSRC (UKRI)Funding Amount
£318,446.00Funder/Grant study page
EPSRCContracted Centre
University of GlasgowPrincipal Investigator
Fani DeligianniPI Contact
Fani.Deligianni@glasgow.ac.ukPI ORCID
0000-0003-1306-5017WHO Catergories
Development of novel therapiesTools and methodologies for interventions
Disease Type
Dementia (Unspecified)CPEC Review Info
| Reference ID | 767 |
|---|---|
| Researcher | Reside Team |
| Published | 24/07/2023 |
Data
| Award Number | EP/W01212X/1 |
|---|---|
| Status / Stage | Active |
| Start Date | 20220918 |
| End Date | 20250917 |
| Duration (calculated) | 02 years 11 months |
| Funder/Grant study page | EPSRC |
| Contracted Centre | University of Glasgow |
| Funding Amount | £318,446.00 |
Plain English Summary
Human motion analysis is a powerful tool in healthcare applications as it has shown to be effective in providing disease progression markers in neurodegenerative conditions such as Alzheimer’s, Parkinson, Amyotrophic Lateral Sclerosis, Huntington’s disease and dementia. On the other hand, deep learning in human motion analysis has shown impressive results in human pose tracking in real-time. This technology can empower patients to have an active role in managing their condition(s), which is a significant objective in a growing e-Health (digital Health) era. Opportunities in digital health initiatives have increased through the response to the pandemic and it has become evident of the need for an intelligent system to detect abnormal changes in patient gait patterns and subsequently alert carers. This technology can also prevent further deterioration (multimorbidity) due to the associated risk of falls and mood disorders.
However, translating recent advances in computer vision in home care is challenging for three major reasons: data privacy, lack of large healthcare labelled data and reduced data quality. This project proposes that data privacy and ethics should be encoded in the algorithms early in the pipeline so that systems are resilient to attacks and do not compromise real-time interaction. We argue that this approach could also improve the performance of the machine learning models with small datasets by focusing on the most relevant features in a data-driven way. Furthermore, we propose that coupling this technology with synthetic data generation can significantly boost the development of ambient sensing technologies for human motion tracking in healthcare applications and develop technology viable for the UK market.
Aims
This project proposes that data privacy and ethics should be encoded in the algorithms early in the pipeline so that systems are resilient to attacks and do not compromise real-time interaction.