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Research Papers

Influence of the Musculotendon Dynamics on the Muscle Force-Sharing Problem of the Shoulder—A Fully Inverse Dynamics Approach

[+] Author and Article Information
Quental Carlos

IDMEC,
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais 1,
Lisboa 1049-001, Portugal
e-mail: carlos.quental@tecnico.ulisboa.pt

Azevedo Margarida

IDMEC,
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais 1,
Lisboa 1049-001, Portugal
e-mail: margarida.azevedo@tecnico.ulisboa.pt

Ambrósio Jorge

IDMEC,
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais 1,
Lisboa 1049-001, Portugal
e-mail: jorge.ambrosio@tecnico.ulisboa.pt

Gonçalves S. B.

IDMEC,
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais 1,
Lisboa 1049-001, Portugal
e-mail: sergio.goncalves@tecnico.ulisboa.pt

Folgado João

IDMEC,
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais 1,
Lisboa 1049-001 Portugal
e-mail: jfolgado@tecnico.ulisboa.pt

Manuscript received November 7, 2017; final manuscript received March 3, 2018; published online April 19, 2018. Assoc. Editor: Paul Rullkoetter.

J Biomech Eng 140(7), 071005 (Apr 19, 2018) (11 pages) Paper No: BIO-17-1509; doi: 10.1115/1.4039675 History: Received November 07, 2017; Revised March 03, 2018

Most dynamic simulations are based on inverse dynamics, being the time-dependent physiological nature of the muscle properties rarely considered due to numerical challenges. Since the influence of muscle physiology on the consistency of inverse dynamics simulations remains unclear, the purpose of the present study is to evaluate the computational efficiency and biological validity of four musculotendon models that differ in the simulation of the muscle activation and contraction dynamics. Inverse dynamic analyses are performed using a spatial musculoskeletal model of the upper limb. The muscle force-sharing problem is solved for five repetitions of unloaded and loaded motions of shoulder abduction and shoulder flexion. The performance of the musculotendon models is evaluated by comparing muscle activation predictions with electromyography (EMG) signals, measured synchronously with motion for 11 muscles, and the glenohumeral joint reaction forces estimated numerically with those measured in vivo. The results show similar muscle activations for all muscle models. Overall, high cross-correlations are computed between muscle activations and the EMG signals measured for all movements analyzed, which provides confidence in the results. The glenohumeral joint reaction forces estimated compare well with those measured in vivo, but the influence of the muscle dynamics is found to be negligible. In conclusion, for slow-speed, standard movements of the upper limb, as those studied here, the activation and musculotendon contraction dynamics can be neglected in inverse dynamic analyses without compromising the prediction of muscle and joint reaction forces.

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Figures

Grahic Jump Location
Fig. 1

Three-element Hill-type muscle model, composed of a CE, a parallel passive elastic element (PE), and a series elastic element (SE). The muscle length lM, tendon length lT, and pennation angle α are also illustrated.

Grahic Jump Location
Fig. 2

Activation of selected muscles during unloaded abduction in the frontal plane versus the humeral elevation with respect to the thorax, in degrees: (a) normalized EMG signal, (b) HmRT model, (c) HmRT+Act model, (d) HmET model, and (e) HmET+Act model. The solid line represents the average of the five repetitions studied and the grey shaded area indicates the corresponding standard deviation. For comparison purposes, each EMG signal is normalized to have the maximum amplitude similar to the maximum muscle activation estimated computationally.

Grahic Jump Location
Fig. 3

Activation of selected muscles during unloaded flexion versus the humeral elevation with respect to the thorax, in degrees: (a) normalized EMG signal, (b) HmRT model, (c) HmRT+Act model, (d) HmET model, and (e) HmET+Act model. The solid line represents the average of the five repetitions studied and the gray shaded area indicates the corresponding standard deviation. For comparison purposes, each EMG signal is normalized to have the maximum amplitude similar to the maximum muscle activation estimated computationally.

Grahic Jump Location
Fig. 4

Glenohumeral joint forces, in KN, versus the humeral elevation with respect to the thorax, in degrees: (a) In Vivo measurements, (b) HmRT model, (c) HmRT+Act model, (d) HmET model, and (e) HmET+Act model. The solid and dashed lines in (a) represent data measured for Patients S2R and S8R, respectively [39]. In the remaining columns, the solid line represents the average of the five repetitions studied and the gray shaded area indicates the corresponding standard deviation.

Grahic Jump Location
Fig. 5

Normalized measure of computational cost, defined as the ratio of the wall-clock time for each simulation to the wall-clock time of the fastest simulation (133 s), for the HmRT, HmRT+Act, HmET, and HmET+Act muscle models. The error bars represent the standard deviation for the five repetitions studied for each motion.

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