Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners.
artificial neural network
human recognition
layer-wise relevance propagation
machine learning
movement pattern
running
triathlon
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
28 Oct 2021
28 Oct 2021
Historique:
received:
06
10
2021
revised:
21
10
2021
accepted:
26
10
2021
entrez:
13
11
2021
pubmed:
14
11
2021
medline:
17
11
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34770451
pii: s21217145
doi: 10.3390/s21217145
pmc: PMC8587997
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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