Context- and Terrain-Aware Gait Analysis
Research type
Research Study
Full title
Context- and Terrain-Aware Gait Analysis and Visualisation
IRAS ID
306353
Contact name
John Mitchell
Contact email
Sponsor organisation
University of Leeds
Duration of Study in the UK
0 years, 3 months, 1 days
Research summary
The average lifespan of individuals in many developed countries is increasing. This factor paired with the increase in global population has the potential to put a strain on healthcare systems with regards to age-related conditions. Particularly, this research considers the impact that conditions such as Parkinson's disease, dementia and stroke have on the walking capabilities on affected individuals.
This research project aims to obtain a gait analysis dataset consisting of sensor data captured during regular daily activities on common terrains such as grass, paving slabs, gravel, etc. The dataset will be collected with a custom sensor system which captures mobility data from a cohort of healthy controls of all ages and people with dementia, Parkinson's disease, stroke survivors, multiple sclerosis, etc. Various machine learning algorithms (custom-implemented using Python) will then be used to determine the walking activity (walking, ramp ascend/descend, stair ascend/descend etc.), the terrain (grass, pavement, carpet etc.), and various walking-related parameters (step length, step height, cadence etc.). It is our hope that these features will enable remote gait analysis to be performed with sufficient contextual information to enable remote diagnosis and rehabilitation tracking for those at risk of falling.
This project has already received ethical approval from the University of Leeds for trials involving healthy participants. We now wish to seek approval to include participants with the previously mentioned gait-affecting conditions.
REC name
North West - Liverpool Central Research Ethics Committee
REC reference
23/NW/0259
Date of REC Opinion
3 Oct 2023
REC opinion
Further Information Favourable Opinion