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Identification of synergies by optimization of trajectory tracking tasks


Alessandro, Cristiano; Nori, Francesco (2012). Identification of synergies by optimization of trajectory tracking tasks. In: 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2012), Rome, Italy, 24 July 2012 - 27 July 2012, 924-930.

Abstract

According to the model of muscle synergies, the central nervous system (CNS) is organised in a modular structure, such that any muscle activation can be produced as a linear superposition of predefined time-varying profiles (i.e. synergies). This organisation might contribute to simplify the control of the musculoskeletal apparatus. Taking inspiration from these findings, we propose a method to identify the synergies that can be used to control a given dynamical system for the task of tracking a set of trajectories. Further, we show how the same approach can be applied to assess the impact of the number of synergies on the performance of the control method. From the theoretical point of view, we provide a novel interpretation of synergies inspired by the Karhunen-Loève decomposition; furthermore, our method suggests that the quality of a set of synergies should be measured in task space rather then in input space.

Abstract

According to the model of muscle synergies, the central nervous system (CNS) is organised in a modular structure, such that any muscle activation can be produced as a linear superposition of predefined time-varying profiles (i.e. synergies). This organisation might contribute to simplify the control of the musculoskeletal apparatus. Taking inspiration from these findings, we propose a method to identify the synergies that can be used to control a given dynamical system for the task of tracking a set of trajectories. Further, we show how the same approach can be applied to assess the impact of the number of synergies on the performance of the control method. From the theoretical point of view, we provide a novel interpretation of synergies inspired by the Karhunen-Loève decomposition; furthermore, our method suggests that the quality of a set of synergies should be measured in task space rather then in input space.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:27 July 2012
Deposited On:29 Jan 2013 09:13
Last Modified:05 Aug 2017 23:15
Publisher:IEEE
ISBN:978-1-4577-1199-2 (P) 978-1-4577-1200-5 (E)
Additional Information:© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher DOI:https://doi.org/10.1109/BioRob.2012.6290701
Related URLs:http://www.biorob2012.org/
Other Identification Number:merlin-id:7789

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