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.

Influence of Gait Cycle Normalization on Principal Activations / Dotti, Gregorio; Ghislieri, Marco; Rosati, Samanta; Agostini, Valentina; Knaflitz, Marco; Balestra, Gabriella. - ELETTRONICO. - (2021), pp. 1-5. ((Intervento presentato al convegno 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) tenutosi a Neuchâtel, Switzerland nel June 23rd to 25th, 2021 [10.1109/MeMeA52024.2021.9478738].

Influence of Gait Cycle Normalization on Principal Activations

Dotti, Gregorio;Ghislieri, Marco;Rosati, Samanta;Agostini, Valentina;Knaflitz, Marco;Balestra, Gabriella
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2913034