How muscle architecture and moment arms affect wrist flexion-extension moments

doi:10.1016/S0021-9290(97)00015-8

Journal of Biomechanics

Volume 30, Issue 7, July 1997, Pages 705-712

https://doi.org/10.1016/S0021-9290(97)00015-8 Get rights and content

Abstract

The purpose of this investigation was to determine how the moment arms and architecture of the wrist muscles influence their isometric moment-generating characteristics. A three-dimensional computer graphics model was developed that estimates the moment arms, maximum isometric forces, and maximum isometric flexion-extension moments generated by 15 muscles about the wrist over a range of wrist flexion angles. In combination with measurements of muscle strength, we used this model to answer three questions: (1) why is peak wrist flexion moment greater than peak extension moment, (2) why does flexion moment vary more with wrist flexion angle than does extension moment, and (3) why does flexion moment peak with the wrist in a flexed position? Analysis of the model revealed that the peak flexion moment is greater than the peak extension moment primarily because of the larger (110%) summed physiologic cross-sectional area of the flexors. The larger variation of flexion moment with flexion angle is caused mainly by greater variation of the moment arms of the major wrist flexors with flexion angle. The location of the peak flexion moment is determined by the wrist flexion moment arms (which tend to increase with wrist flexion) in combination with the force-length characteristics of these muscles.

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Examining kinematics and muscle activity of the upper extremity while performing cleaning tasks: A pre-post shift evaluation
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Custodians engage in strenuous manual labour throughout their workday. Uncertainty exists on whether kinematics or muscle activation changes as workers progress through their shift. The purpose of this study was to examine muscle activation and upper extremity kinematics during typical custodial tasks performed at the start and end of the work shift. Electromyography (EMG) of 8 torso and upper extremity muscles and motion capture of the corresponding region were collected for 10 custodial participants while they completed garbage removal, dry mopping, and vacuuming tasks at the start and end of a work shift. The start of shift assessment demonstrated larger anterior deltoid, posterior deltoid and extensor digitorum activation by up to 12.6 %maximal voluntary contraction (%MVC) (p < 0.001–0.023). Task affected mean and peak EMG in all muscles except flexor digitorum (p < 0.001–0.0293), and the greatest activation was found during vacuuming (peak <55%MVC in anterior and middle deltoid) and the heaviest garbage removal task (84% MVC peak in upper trapezius). Mopping created the lowest amounts of activation for almost all muscles. Joint range of motion (p < 0.001–0.031) in the shoulder was highest in vacuuming, while trunk flexion was largest in garbage removal tasks. This work replicated common custodial tasks in a laboratory, using actual custodial workers at times relevant to their typical work shifts. The information presented is useful for ergonomists, work tasks designers and custodial administration staff to develop guidelines for injury prevention.
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EMG-driven neuromusculoskeletal models have been used to study many impairments and hold great potential to facilitate human–machine interactions for rehabilitation. A challenge to successful clinical application is the need to optimize the model parameters to produce accurate kinematic predictions. In order to identify the key parameters, we used Monte-Carlo simulations to evaluate the sensitivities of wrist and metacarpophalangeal (MCP) flexion/extension prediction accuracies for an EMG-driven, lumped-parameter musculoskeletal model. Four muscles were modeled with 22 total optimizable parameters. Model predictions from EMG were compared with measured joint angles from 11 able-bodied subjects. While sensitivities varied by muscle, we determined muscle moment arms, maximum isometric force, and tendon slack length were highly influential, while passive stiffness and optimal fiber length were less influential. Removing the two least influential parameters from each muscle reduced the optimization search space from 22 to 14 parameters without significantly impacting prediction correlation (wrist: 0.90 ± 0.05 vs 0.90 ± 0.05, p = 0.96; MCP: 0.74 ± 0.20 vs 0.70 ± 0.23, p = 0.51) and normalized root mean square error (wrist: 0.18 ± 0.03 vs 0.19 ± 0.03, p = 0.16; MCP: 0.18 ± 0.06 vs 0.19 ± 0.06, p = 0.60). Additionally, we showed that wrist kinematic predictions were insensitive to parameters of the modeled MCP muscles. This allowed us to develop a novel optimization strategy that more reliably identified the optimal set of parameters for each subject (27.3 ± 19.5%) compared to the baseline optimization strategy (6.4 ± 8.1%; p = 0.004). This study demonstrated how sensitivity analyses can be used to guide model refinement and inform novel and improved optimization strategies, facilitating implementation of musculoskeletal models for clinical applications.
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How muscle architecture and moment arms affect wrist flexion-extension moments

Abstract

Med. Engng Phys.

J. Biomechanics

J. Biomechanics

Comput. Biol. Med.

J. Hand Surg.

J. Hand Surg.

J. Hand Surg.

J. Hand Surg.

J. Biomechanics

J. Biomechanics

J. Hand Surg.

J. Hand Surg.

J. Biomechanics

J. Biomechanics