Influence of Gait Cycle Normalization on Principal Activations
Autor: | Marco Knaflitz, Valentina Agostini, Gabriella Balestra, Gregorio Dotti, Samanta Rosati, Marco Ghislieri |
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Rok vydání: | 2021 |
Předmět: |
Normalization (statistics)
EMG gait analysis locomotion muscle activations principal activations time normalization principal activations business.industry Pattern recognition Gait cycle muscle activations locomotion Preferred walking speed Piecewise linear function EMG Similarity (network science) Gait analysis gait analysis Task analysis Artificial intelligence Cluster analysis business time normalization Mathematics |
Zdroj: | MeMeA |
Popis: | The Clustering for Identification of Muscle Activation Pattern (CIMAP) algorithm has been recently proposed to cope with the high intra-subject variability of muscle activation patterns and to allow the extraction of principal activations (PAs), defined as those muscle activation intervals that are strictly necessary to perform a specific task. To assess differences between different PAs, gait cycle normalization techniques are needed to handle between- and within-subject variability. The aim of this contribution is to assess the effect of two different time-normalization techniques (Linear Length Normalization and Piecewise Linear Length Normalization) on PA extraction, in terms of inter-subject similarity. Results demonstrated no statistically significant differences in the inter-subject similarity between the two tested approaches, revealing, on the average, inter-subject similarity values higher than 0.64. Moreover, a statistically significant difference in the inter-subject similarity among muscles was assessed, revealing a higher similarity of PAs extracted considering the distal lower limb muscles. In conclusion, our results demonstrated that PAs extracted from healthy subjects during a walking task at comfortable walking speed are not affected by the time-normalization approach implemented. |
Databáze: | OpenAIRE |
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