An EMG-marker tracking optimisation method for estimating muscle forces

Bélaise, Colombe; Maso, Fabien Dal; Michaud, Benjamin; Mombaur, Katja; Begon, Mickaël

doi:10.1007/s11044-017-9587-2

Cited by 21 publications

(26 citation statements)

References 49 publications

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“…As proposed in Bélaise et al (2018a), controls and state variables were simultaneously optimized using an EMG-marker tracking optimization process. Because the model has a reduced muscular redundancy and because a generic model was used as opposed to a subject-specific model, the optimal maximal isometric forces were also identified during this process.…”

Section: Methodsmentioning

confidence: 99%

“…For example, state-of-the-art algorithms used in conjunction with existing musculoskeletal models–like direct collocation (Santos et al, 2017) and direct multiple shooting (Bélaise et al, 2018a,b)—can be used to solve forward dynamics problems in a timely manner. Recently, Bélaise et al (2018a,b) introduced an EMG-marker tracking optimization method to predict musculo-tendon forces in a co-contraction case. Based on simulated datasets of upper limb movements, the authors showed the importance of tracking both marker trajectories and EMG, in particular to reproduce muscle co-contractions.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of our project is to establish a computational framework appropriate for predictive simulations of healthy and pathological gait, that is able to reproduce muscle co-contractions, and that allows for the introduction of various kind of perturbations on the model (e.g., therapy-related, surgery-related, pathology-related perturbations). This preliminary study represents a first step toward this project by adapting the computational framework proposed by Bélaise et al (2018a,b) to a lower limb musculoskeletal model during gait. This framework has been tested for this purpose on a type of pathological gait known as equinus gait.…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Method Tracking EMG, Ground Reactions Forces, and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait

Moissenet¹,

Bélaise

Piche

et al. 2019

Front. Neurorobot.

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In the context of neuro-orthopedic pathologies affecting walking and thus patients' quality of life, understanding the mechanisms of gait deviations and identifying the causal motor impairments is of primary importance. Beside other approaches, neuromusculoskeletal simulations may be used to provide insight into this matter. To the best of our knowledge, no computational framework exists in the literature that allows for predictive simulations featuring muscle co-contractions, and the introduction of various types of perturbations during both healthy and pathological gait types. The aim of this preliminary study was to adapt a recently proposed EMG-marker tracking optimization process to a lower limb musculoskeletal model during equinus gait, a multiphase problem with contact forces. The resulting optimization method tracking EMG, ground reactions forces, and marker trajectories allowed an accurate reproduction of joint kinematics (average error of 5.4 ± 3.3 mm for pelvis translations, and 1.9 ± 1.3° for pelvis rotation and joint angles) and ensured good temporal agreement in muscle activity (the concordance between estimated and measured excitations was 76.8 ± 5.3 %) in a relatively fast process (3.88 ± 1.04 h). We have also highlighted that the tracking of ground reaction forces was possible and accurate (average error of 17.3 ± 5.5 N), even without the use of a complex foot-ground contact model.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Optimization Method Tracking EMG, Ground Reactions Forces, and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait

Moissenet¹,

Bélaise

Piche

et al. 2019

Front. Neurorobot.

Self Cite

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“…Although assessing the effect of such an underestimation on our outcomes is not straightforward, it may be limited since trapezius and serratus anterior are not inserted on the humerus and do not affect directly glenohumeral joint stability. Advanced musculoskeletal algorithms using the EMG as input [ 45 ] could be implemented in future studies.…”

Section: Discussionmentioning

confidence: 99%

Muscle function in glenohumeral joint stability during lifting task

et al. 2017

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Ensuring glenohumeral stability during repetitive lifting tasks is a key factor to reduce the risk of shoulder injuries. Nevertheless, the literature reveals some lack concerning the assessment of the muscles that ensure glenohumeral stability during specific lifting tasks. Therefore, the purpose of this study was to assess the stabilization function of shoulder muscles during a lifting task. Kinematics and muscle electromyograms (n = 9) were recorded from 13 healthy adults during a bi-manual lifting task performed from the hip to the shoulder level. A generic upper-limb OpenSim model was implemented to simulate glenohumeral stability and instability by performing static optimizations with and without glenohumeral stability constraints. This procedure enabled to compute the level of shoulder muscle activity and forces in the two conditions. Without the stability constraint, the simulated movement was unstable during 74%±16% of the time. The force of the supraspinatus was significantly increased of 107% (p<0.002) when the glenohumeral stability constraint was implemented. The increased supraspinatus force led to greater compressive force (p<0.001) and smaller shear force (p<0.001), which contributed to improved glenohumeral stability. It was concluded that the supraspinatus may be the main contributor to glenohumeral stability during lifting task.

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“…This curve can be solved by various approaches, such as the traditional variational method [24,25] and the popular strategies like direct methods and indirect methods. The direct methods include the shooting method and multiple shooting method [26,27], while the indirect methods include the discrete mechanics and optimal control (DMOC) method and Gauss pseudospectral method [28][29][30]. Here, the traditional method is adopted for its robustness and stability.…”

Section: Optimal Control Solutionmentioning

confidence: 99%

Optimal Logistics Control of an Omnichannel Supply Chain

et al. 2019

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This paper aims to find the best way to control logistics in an omnichannel supply chain (OSC). For this purpose, two steps of work were carried out around case-based reasoning (CBR). In the first step, the combined feedback which proved stability was selected to control logistics in the single node, while the variational method and the virtual siphon were combined to determine the optimal control curve. There is a linear part and a nonlinear part in the combined feedback. The new method of storing data mode is “data turning to picture”. In the second step, image features were extracted by the hybrid method of SURF-GoogLeNet and used for case matching via the grey cloud method. SURF-GoogLeNet was firstly used to update the weight proportion of the defect points in the whole image via the speeded up robust features (SURF) method and secondly to self-extract features using the GoogLeNet method. Finally, the effectiveness of the proposed methods was verified through experiments. The research findings shed new light on the management of supply chains.

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An EMG-marker tracking optimisation method for estimating muscle forces

Cited by 21 publications

References 49 publications

An Optimization Method Tracking EMG, Ground Reactions Forces, and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait

An Optimization Method Tracking EMG, Ground Reactions Forces, and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait

Muscle function in glenohumeral joint stability during lifting task

Optimal Logistics Control of an Omnichannel Supply Chain

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