Abstract
Human movement patterns were shown to be as unique to individuals as their fingerprints. However, some movement characteristics are more important than other characteristics for machine learning algorithms to distinguish between individuals. Here, we explored the idea that movement patterns contain unique characteristics that differentiate between individuals and generic characteristics that do not differentiate between individuals. Layer-wise relevance propagation was applied to an artificial neural network that was trained to recognize 20 male triathletes based on their respective movement patterns to derive characteristics of high/low importance for human recognition. The similarity between movement patterns that were defined exclusively through characteristics of high/low importance was then evaluated for all participants in a pairwise fashion. We found that movement patterns of triathletes overlapped minimally when they were defined by variables that were very important for a neural network to distinguish between individuals. The movement patterns overlapped substantially when defined through less important characteristics. We concluded that the unique movement characteristics of elite runners were predominantly sagittal plane movements of the spine and lower extremities during mid-stance and mid-swing, while the generic movement characteristics were sagittal plane movements of the spine during early and late stance.
Highlights
Movement characteristics appear to be similar across individuals and/or functional groups
In the context of human recognition based on movement patterns, we propose that variables with high relevance scores may encode unique characteristics of movement patterns, while variables with low relevance scores may encode the more generic features of movement patterns
The purpose of this work was to isolate the unique and generic movement characteristics of elite-level triathletes and to understand if they are expressed by variables with high/low relevance scores derived from a neural network trained to recognize athletes based on their running patterns
Summary
Movement characteristics appear to be similar across individuals and/or functional groups. Pataky et al [6], for instance, accurately identified 104 individuals based only on their plantar pressure distribution during walking. It appears, that certain characteristics of human movement are shared amongst individuals/functional groups while other movement characteristics appear to be unique. That certain characteristics of human movement are shared amongst individuals/functional groups while other movement characteristics appear to be unique Supporting this notion, our recent work highlighted that some movement characteristics (e.g., joint angles in the coronal and transverse plane) were highly important for the identification of a specific individual within a cohort of novice runners, while other movement characteristics (e.g., joint angles in the sagittal plane) were less important for said identification [7]. A gait-based identification system that considers only unique movement characteristics, for example, may be more difficult to breach, and the generic movement characteristics of a population of elite marathon athletes may isolate biomechanically relevant aspects of an efficient running style
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