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Article

An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

1
Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
2
PoliToBIOMed Lab, Politecnico di Torino, 10129 Turin, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Iosa
Sensors 2021, 21(10), 3311; https://doi.org/10.3390/s21103311
Received: 31 March 2021 / Revised: 29 April 2021 / Accepted: 7 May 2021 / Published: 11 May 2021
In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds. View Full-Text
Keywords: gait; locomotion; motor module; number of synergies; VAF gait; locomotion; motor module; number of synergies; VAF
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MDPI and ACS Style

Ballarini, R.; Ghislieri, M.; Knaflitz, M.; Agostini, V. An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking. Sensors 2021, 21, 3311. https://doi.org/10.3390/s21103311

AMA Style

Ballarini R, Ghislieri M, Knaflitz M, Agostini V. An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking. Sensors. 2021; 21(10):3311. https://doi.org/10.3390/s21103311

Chicago/Turabian Style

Ballarini, Riccardo, Marco Ghislieri, Marco Knaflitz, and Valentina Agostini. 2021. "An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking" Sensors 21, no. 10: 3311. https://doi.org/10.3390/s21103311

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