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.
Highlights
Collecting large datasets of amputee gait is notoriously difficult
To allow for validation of our results against existing literature, we focus our analysis on an example dataset of unilateral trans-femoral amputees level walking at a self-selected speed and compare our data against published values
The results for gait cycle time, stance times, stance duty factors and asymmetry index can be seen in Table 2 and Figs 1 and 2
Summary
Collecting large datasets of amputee gait is notoriously difficult. Especially, for less prevalent amputation levels and for elderly vascular amputees (aged over 65), the largest and most challenging to rehabilitate section of the amputee population [1]. A few recent studies have begun to explore amputee walking on surfaces representative of the everyday environment [3,4,5,6,7] many, if not most, amputee gait studies are conducted in gait laboratories, which greatly limits the extent to which results can be generalised. This is especially important, since walking in the community involves many surfaces that are not level, smooth or flat, for example, stairs, ramps, gravel paths, grass
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