Biomechanics of the Upper Limbs: Mechanics, Modelling and Musculoskeletal Injuries

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However, despite a spike in the number of work-related musculoskeletal disorders WRMSDs in the upper limbs—due to a sharp increase in the amount of computer-related jobs—few if any books have focused exclusively on WRMSDs, until now. Biomechanics of the U The 12th edition of Methods, Standards, and Work Design will pr In addition, interest in upper extremity musculoskeletal disorders has grown as the service sector has claimed a larger share of the workforce. These factors introduce the need for an up-to-date text that combines basic biomechanics with practical bioengineering iss Cost-effectiveness and product reliability without excess capacity are the keys to successful activity in business, industry, and government, and these keys are the end results of methods engineering.

The 11th edition of Methods, Standards, and Work Design provides practical, up-to-date descr The 13th edition of Methods, Standards, and Work Design will pro This text combines basic biomechanics with practical bioengineering issues and provides more than the general introductions to cumulative trauma disorders CTDs and medical management-related books currently on the market.

Biomechanics of the Upper Limbs: Mechanics William Stanek. Eric Noreen. Cost-effectiveness and product reliability without excess capacity are the keys to successful activity in business, industry, and government. These keys are the end results of methods engineering. The 13th edition of Methods, Standards, and Work Design will pr However, despite a spike in the number of work-related musculoskeletal disorders WRMSDs in the upper limbs—due to a sharp increase in the amount of computer-related jobs—few if any books have focused exclusively on WRMSDs, until now.

Biomechanics of the U The 12th edition of Methods, Standards, and Work Design will pr In addition, interest in upper extremity musculoskeletal disorders has grown as the service sector has claimed a larger share of the workforce. These factors introduce the need for an up-to-date text that combines basic biomechanics with practical bioengineering iss Comparison of tendon excursion-joint angle relationships with the experimental data reported in Horii et al.

Experimental data was taken from Brand and Hollister 4 in left panels, Horii et al. The last three lower panels present the results of, from top to bottom, the initial model, the model using physiological cross-sectional area PCSA values from Chao et al. Compared to the initial model, the two alternative versions of the model resulted in different patterns of muscle forces and muscle load sharing. The use of PCSAs from the literature particularly modified the load sharing between the extensors. Using moment arms from the literature, the load sharing between both flexors and extensors was modified.

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In both motions, this model resulted in modified muscle load sharing of the flexors on the interval between 0. During the palm-down motion, the extensors were particularly affected in the interval between 0. During the thumb-up motion, although muscle force intensities were not modified greatly, ECRL was active throughout the trial, including between 0. A large anatomical dataset was collected to provide a quantified representation of the musculoskeletal system from the elbow to the tip of the five fingers for musculoskeletal modelling of the hand and the wrist.

Motion capture, muscle fixation and clinical imaging were combined to characterize the musculoskeletal geometry, the muscle morphology, and the bone surfaces of a single specimen. The collection protocol followed the one described in Mirakhorlo et al. Compared to this previous study, the specimen dissected here was male, younger, and larger in terms of anthropometry. In addition, the present dataset also provides clinical imaging both CT and MRI which was not available with the previous study. Finally, simple geometrical shapes were proposed here to constrain the paths of tendons at the wrist, which was not available previously.

A musculoskeletal model of the wrist, with two degrees of freedom, actuated by seven muscles, was developed to estimate muscle forces and demonstrate the potential of the collected dataset. The tendon excursions in the model, calculated using the two-cylinder approach Fig. The comparison of moment arms in the model with those measured functionally on cadavers showed that the distribution across muscles were in relatively good agreement but that the shapes of the curves occasionally differed Fig.

The general discrepancies between modelled and experimental values in radial—ulnar deviation can be explained by the fact that the two-cylinder approach predominantly constrains the tendon paths during flexion—extension. Nevertheless, the curves taken from cadaveric studies are mean curves and therefore are representative of trends in a population, whereas the model represents a single specimen.

Furthermore, none of these studies provided a measure of the inter-subject variations; therefore, although the curves of the model diverged from those of the literature, they might remain within a physiological range of the mean curves. Finally, the anthropometry of the specimens in those previous studies was not provided; therefore, it is not possible to normalize the moment arms and remove any size effects in the comparisons. The muscle forces estimated by the model during the two simulated motions were consistent with the mechanical constraints of the motion and included estimations of co-contraction Figs.

The muscles that were most activated throughout the flexion—extension cycle were the extensors in the palm-down position and the radial deviators in the thumb-up position. This is explained by the fact that these muscle groups were the ones balancing the action of gravity on the hand, which resulted in a flexion moment in the palm-down position and an ulnar moment in the thumb-up position.

In both positions, most of the flexors and the extensors reached their maximal muscle force at the times corresponding to the maximal flexion angle and the maximal extension angle, respectively. This is consistent with the development of contributions from passive structures, which created a resistive moment that increased exponentially when approaching the limits of the range of motion and ultimately outweighed the hand acceleration moment.

More interestingly, in the interval where extension angle was maximal, i. This co-contraction was due to the constraint on the direction of the joint reaction force, preventing unrealistically high shear components. This result is encouraging because the estimation of co-contraction using musculoskeletal modelling is difficult and often requires the use of electromyography to guide the choice of a solution in the muscle load sharing problem.

Unfortunately, because of the lack of data regarding muscular activity and loading of the forearm muscles during simple wrist tasks, it is difficult to fully validate the muscle force and co-contraction levels estimated by the model. However, the tendon excursion and moment arms estimated by the model were consistent with experimental data giving confidence that the model is realistic.

The two alternative versions of the model that combined data collected in the present study with average data previously reported in the literature markedly modified the muscle force levels and the muscle load sharing predicted by the model Figs. Using average PCSAs particularly modified the load sharing between the extensors. This could be expected since the PCSAs of extensors were markedly larger in the current study whereas those of flexors were closer to literature data Fig. The muscle stress criteria Eq.

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Therefore, if a given muscle is assigned a larger PCSA, the optimisation will try to reduce the contribution of that muscle. In general, the variations in and distribution of these parameters, are probably due to differences in terms of age or anthropometry between the specimen dissected in this study and the populations considered in the previous studies.


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As with PCSAs, the distribution of moment arms across the muscles directly impacts muscle load sharing since it represents how much each muscle can contribute to balance the resultant moment at the joint Eq. Although the moment arms in the initial model were globally close to those of Brand and Hollister 4 Fig.

More importantly, the regression equations taken from the literature assumed the moment arm about an axis varies only with the joint angle about that same axis and thus neglected the fact that each moment arm component depends on the posture of the joint, which is described by two joint angles for the wrist. Using 3D coordinates of the tendon via points in combination with geometrical constraints, as in the initial model, represents a more realistic representation of the joint biomechanics.

The two alternative versions of the model demonstrated how much the combination of different datasets within a same model can markedly influence the predicted muscle load sharing, and confirmed the importance of using the same source of data for musculoskeletal geometry and muscle morphology. Several limitations should be considered regarding the results of the present study.

Regarding the dataset, the parameters provided herein describe the musculoskeletal geometry and muscle morphology of a single specimen and might be inadequate to represent the anatomy of some subjects.

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Further studies should therefore investigate how to personalize these parameters so that the dataset can be used to model participants presenting different anthropometries or different muscle force capacities. Furthermore, the bone surfaces obtained from the clinical imaging were manually registered to match the data measured with motion capture during the dissection.

Automated registration may have been possible if the clusters were affixed to the specimen prior to imaging, but this would have required dissection prior to the scans, which would have complicated the scanning protocol. In addition, a greater number of scans would have been required, as clusters could not be placed on all the fingers at once. More generally, despite the quality and quantity of data provided in the present study, the users of this dataset should bear in mind that it faces inherent limitations related to all cadaveric experiments.

For instance, the parameters provided might vary with joint motions or muscle contraction levels, e. Considering the high number of muscles tested, investigating such variations would have drastically increased the time of the dissection, detrimentally affecting the integrity of the tissues. However, we are confident that the quantitative dataset provided in this study represents a reliable reference to design models of the musculoskeletal system of the hand and the forearm. The action of hand extrinsic muscles at the wrist was not included in the model.

Although this might have influenced the muscle load sharing predicted by the model, adding these muscles would have necessitated the inclusion of the degrees of freedom at the finger joints, as well as the actions of the intrinsic hand muscles at those joints. However, our intentions were to demonstrate the potential of the dataset using a relatively simple musculoskeletal model of the wrist, and to illustrate the consequences of combining datasets.

In conclusion, the anatomical dataset provided here will enable the development of a complex model of the musculoskeletal system of the hand and wrist, from the elbow to the tips of the fingers. The wrist musculoskeletal model presented herein demonstrated that the dataset can provide physiologically realistic estimations of tendon excursions and moment arms and can be further used to predict muscle forces during simple motions. This large dataset can be used to develop and improve musculoskeletal models of the hand, and therefore, ameliorate the predictions of the geometrical and biomechanical behaviour of the hand structure.

This could facilitate the quantitative assessment of hand internal biomechanics and therefore, improve ergonomics, rehabilitation and the prevention of musculoskeletal disorders.

Pathophysiological mechanisms of musculoskeletal disorders

This sponsor had no role in the study design or the writing of the manuscript, or the decision to submit the manuscript for publication. We would like to thank Pr. Guus Baan for their help during the design of the protocol. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Open Access.


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  8. First Online: 02 October Introduction The hand is essential for daily living. Owing to the technical challenges of simultaneously tracking all 22 segments, the clusters were designed to be mounted and dismounted on different segments. An M4 ball and spring plunger system ensured accurate and repeatable cluster placement. Open image in new window.

    Interpreting Upper Limb Dynamic Simulations in the Context of Clinical Variability

    A musculoskeletal model of the wrist was created based on the anatomical dataset. The wrist was modeled as two hinge joints between the radius and MC3, one representing radial—ulnar deviation and one representing flexion—extension. The joint centre of rotation was assumed to be the point at the intersection of the flexion—extension axis and the plane parallel to the sagittal plane of the radius, defined using ISB definitions, 29 and containing the midpoint between the radial and ulnar styloids Fig.

    Only the prime movers of the wrist, i. Anatomical Dataset In total, the anatomical dataset includes 52 landmarks, muscle points, 24 functional axes as well as the morphological parameters of 48 muscles and muscle bellies.

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