Crowd-Sourced Amputee Gait Data: A Feasibility Study Using YouTube Videos of Unilateral Trans-Femoral Gait.

Abstract

Collecting large datasets of amputee gait data is notoriously difficult. Additionally, collecting data on less prevalent amputations or on gait activities other than level walking and running on hard surfaces is rarely attempted. However, with the wealth of user-generated content on the Internet, the scope for collecting amputee gait data from alternative sources other than traditional gait labs is intriguing. Here we investigate the potential of YouTube videos to provide gait data on amputee walking. We use an example dataset of trans-femoral amputees level walking at self-selected speeds to collect temporal gait parameters and calculate gait asymmetry. We compare our YouTube data with typical literature values, and show that our methodology produces results that are highly comparable to data collected in a traditional manner. The similarity between the results of our novel methodology and literature values lends confidence to our technique. Nevertheless, clear challenges with the collection and interpretation of crowd-sourced gait data remain, including long term access to datasets, and a lack of validity and reliability studies in this area.