Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation.

ABSTRACT: This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and walked on an improvised route across a variety of terrains in the vicinity of their homes. Their physical activity data were clustered to extract 'unique' groupings in a low-dimension feature space in an unsupervised learning approach, and an algorithm was created to automatically distinguish such activities. After testing three dimensionality reduction methods-namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approx

SUBMITTER: Jamieson A 

PROVIDER: S-EPMC10575014 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature