A biomechanics-based method for the quantification of muscle selectivity in a musculoskeletal system

Jun Yao, Anamaria Acosta, Julius P A Dewald*

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, we have developed a novel and simple method to quantify the ability to selectively activate our muscles in an effective pattern to achieve a particular task. In the context of this study, we define an effective pattern as that in which muscles whose mechanical contribution to the task is greatest, are mostly active, while the antagonist muscles are mostly silent. This new method uses biomechanical parameters to project the multi-channel EMGs into a three-dimensional artificial torque space, where the EMGs are represented as muscle activation vectors. Using the muscle activation vectors we defined a simple scalar, the muscle selection index, to quantify muscle selectivity. We demonstrate that by using this index we are able to quantify the muscle selectivity during the generation of isometric shoulder or elbow torques in brain-injured and able-bodied subjects. This method can be used during both static and dynamic motor tasks in a multi-articular musculoskeletal system.

Original languageEnglish (US)
Pages (from-to)1527-1530
Number of pages4
JournalJournal of Biomechanics
Volume39
Issue number8
DOIs
StatePublished - May 15 2006

Fingerprint

Musculoskeletal system
Musculoskeletal System
Biomechanics
Biomechanical Phenomena
Muscle
Muscles
Torque
Chemical activation
Elbow
Brain
Joints

Keywords

  • Brain injury
  • Multi-joint motor tasks
  • Muscle coordination
  • Muscle selectivity
  • Musculoskeletal system

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine

Cite this

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title = "A biomechanics-based method for the quantification of muscle selectivity in a musculoskeletal system",
abstract = "In this paper, we have developed a novel and simple method to quantify the ability to selectively activate our muscles in an effective pattern to achieve a particular task. In the context of this study, we define an effective pattern as that in which muscles whose mechanical contribution to the task is greatest, are mostly active, while the antagonist muscles are mostly silent. This new method uses biomechanical parameters to project the multi-channel EMGs into a three-dimensional artificial torque space, where the EMGs are represented as muscle activation vectors. Using the muscle activation vectors we defined a simple scalar, the muscle selection index, to quantify muscle selectivity. We demonstrate that by using this index we are able to quantify the muscle selectivity during the generation of isometric shoulder or elbow torques in brain-injured and able-bodied subjects. This method can be used during both static and dynamic motor tasks in a multi-articular musculoskeletal system.",
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N2 - In this paper, we have developed a novel and simple method to quantify the ability to selectively activate our muscles in an effective pattern to achieve a particular task. In the context of this study, we define an effective pattern as that in which muscles whose mechanical contribution to the task is greatest, are mostly active, while the antagonist muscles are mostly silent. This new method uses biomechanical parameters to project the multi-channel EMGs into a three-dimensional artificial torque space, where the EMGs are represented as muscle activation vectors. Using the muscle activation vectors we defined a simple scalar, the muscle selection index, to quantify muscle selectivity. We demonstrate that by using this index we are able to quantify the muscle selectivity during the generation of isometric shoulder or elbow torques in brain-injured and able-bodied subjects. This method can be used during both static and dynamic motor tasks in a multi-articular musculoskeletal system.

AB - In this paper, we have developed a novel and simple method to quantify the ability to selectively activate our muscles in an effective pattern to achieve a particular task. In the context of this study, we define an effective pattern as that in which muscles whose mechanical contribution to the task is greatest, are mostly active, while the antagonist muscles are mostly silent. This new method uses biomechanical parameters to project the multi-channel EMGs into a three-dimensional artificial torque space, where the EMGs are represented as muscle activation vectors. Using the muscle activation vectors we defined a simple scalar, the muscle selection index, to quantify muscle selectivity. We demonstrate that by using this index we are able to quantify the muscle selectivity during the generation of isometric shoulder or elbow torques in brain-injured and able-bodied subjects. This method can be used during both static and dynamic motor tasks in a multi-articular musculoskeletal system.

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