A new musculoskeletal AnyBody™ detailed hand model

Engelhardt, Lucas; Melzner, Maximilian; Havelková, Linda; Fiala, Pavel; Christen, Patrik; Dendorfer, Sebastian; Simon, Ulrich

doi:10.1080/10255842.2020.1851367

Cited by 19 publications

(27 citation statements)

References 34 publications

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“…published by Engelhardt and Melzner et al (2020) and thus to determine whether the model can be applied to a broad population outside the average hand for patient-specific modeling. Examining the comparison between experimental data, the standard model, and LHS simulations suggests that the hand model is able to provide convergence for a broad range of input data and, therefore, can be considered relatively stable.…”

Section: Discussionmentioning

confidence: 99%

“…The study presented a sensitivity and stability analysis of the musculoskeletal hand model by Engelhardt and Melzner et al (2020) by changing the input parameters of the muscles' line of action. The results indicate the model's corresponding stability, which provides a reasonable basis for the patient-specific simulation of a broad population regarding the hand.…”

Section: Discussionmentioning

confidence: 99%

“…The PCSA and the muscles' line of action are crucial parameters in determining acting muscle forces and hence the acting joint reaction forces. In order to evaluate the sensitivity of the recently published musculoskeletal hand model from Engelhardt and Melzner et al (2020) to these parameters, the underlying anatomical data (Havelkov a et al 2020) was varied according to their known measurement uncertaintiessee Appendix A, supplementary material. Subsequently, the simulated on-and off-set time points of the muscle activities were compared with experimentally derived electromyographic activities.…”

Section: Methodsmentioning

confidence: 99%

“…The recently published hand model by Engelhardt and Melzner et al (2020) within the AnyBody TM Modeling System (AMS) (Anybody, Aalborg, Denmark) is based on a general data set, which includes data of 16 donors (Havelkov a et al 2020). This model consists of 80 extrinsic and intrinsic hand muscles with 31 degrees of freedom and has been shown to correlate highly with moment arm studies from prior literature (Engelhardt and Melzner et al 2020). Since this model's anatomical data has some unavoidable known indeterminacy, the outcome sensitivity of subject-specific model predictions (muscle activities and joint reaction forces) compared to the input parameters should be researched.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The impact of anatomical uncertainties on the predictions of a musculoskeletal hand model – a sensitivity study

Melzner

Süß

Dendorfer

2021

Computer Methods in Biomechanics and Biomedical Engineering

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The impact of anatomical uncertainties on the predictions of a musculoskeletal hand model – a sensitivity study

Melzner

Süß

Dendorfer

2021

Computer Methods in Biomechanics and Biomedical Engineering

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“…In contrast to implanted sensors for experimental measurements, this simulation approach is ethically not questionable and adaptable for parameter studies. The work of Engelhardt and Melzner et al [1] introduced a musculoskeletal hand model developed for inverse-dynamics simulations with the AnyBody Modeling System™ (AMS). This was used to further analyze the resulting human hand joint and muscle forces in an ongoing motion sequence.…”

Section: Introductionmentioning

confidence: 99%

Hand Motion Capture from a 3D Leap Motion Controller for a Musculoskeletal Dynamic Simulation

Fonk

Schneeweiss

Simon

et al. 2021

Sensors

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The AnyBody Modeling System™ (AMS) is a musculoskeletal software simulation solution using inverse dynamics analysis. It enables the determination of muscle and joint forces for a given bodily motion. The recording of the individual movement and the transfer into the AMS is a complex and protracted process. Researches indicated that the contactless, visual Leap Motion Controller (LMC) provides clinically meaningful motion data for hand tracking. Therefore, the aim of this study was to integrate the LMC hand motion data into the AMS in order to improve the process of recording a hand movement. A Python-based interface between the LMC and the AMS, termed ROSE Motion, was developed. This solution records and saves the data of the movement as Biovision Hierarchy (BVH) data and AnyScript vector files that are imported into the AMS simulation. Setting simulation parameters, initiating the calculation automatically, and fetching results is implemented by using the AnyPyTools library from AnyBody. The proposed tool offers a rapid and easy-to-use recording solution for elbow, hand, and finger movements. Features include animation, cutting/editing, exporting the motion, and remote controlling the AMS for the analysis and presentation of musculoskeletal simulation results. Comparing the motion tracking results with previous studies, covering problems when using the LMC limit the correctness of the motion data. However, fast experimental setup and intuitive and rapid motion data editing strengthen the use of marker less systems as the herein presented compared to marker based motion capturing.

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Musculoskeletal simulation of elbow stability for common injury patterns

Melzner

Pfeifer

Süß

et al. 2022

Journal Orthopaedic Research

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Elbow stability is derived from a combination of muscular, ligamentous, and bony structures. After an elbow trauma the stability of the joint is an important decision criterion for the subsequent treatment. The decision regarding nonoperative/operative care depends mostly on subjective assessments of medical experts. Therefore, the aim of this study is to use musculoskeletal simulations as an objective assessment tool to investigate the extent to which failure of different stabilizers affects the elbow stability and how these observations correspond to the assessment from clinical practice. A musculoskeletal elbow simulation model was developed for this aim. To investigate the stability of the elbow, varus/valgus moments were applied under 0°, 45°, and 90° flexion while the respective cubital angle was analyzed. This was performed for nine different injury scenarios, which were also evaluated for stability by clinical experts. With the results, it can be determined by which injury pattern and under which flexion angle the elbow stability is impaired regarding varus/valgus moments. The scenario with a complete failure of the medial and lateral ligaments and a fracture of the radial head was identified as having the greatest instability. The study presented a numerical determination of elbow stability against varus/valgus moments regarding clinical injury patterns, as well as a comparison of the numerical outcome with experience gained in clinical practice. The numerical predictions agree well with the assessments of the clinical specialists. Thus, the results from musculoskeletal simulation can make an important contribution to a more objective assessment of the elbow stability.

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A new musculoskeletal AnyBody™ detailed hand model

Cited by 19 publications

References 34 publications

The impact of anatomical uncertainties on the predictions of a musculoskeletal hand model – a sensitivity study

The impact of anatomical uncertainties on the predictions of a musculoskeletal hand model – a sensitivity study

Hand Motion Capture from a 3D Leap Motion Controller for a Musculoskeletal Dynamic Simulation

Musculoskeletal simulation of elbow stability for common injury patterns

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