Context- and Terrain-aware Gait Analysis

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.

NCT Number	NCT07491588
Lead Sponsor	University of Leeds
Conditions	Parkinson's Disease and Parkinsonism, Dementia, Acquired Brain Injury, Multiple Sclerosis, Lower Limb Amputation Below Knee (Injury)
Start Date	2025-03-01
Primary Completion	2025-07 (estimated)
Study Completion	2025-07 (estimated)
Updated on ClinicalTrials.gov	2026-03-24