Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization

Sartori, Massimo; Farina, Dario; Lloyd, David G.

doi:10.1016/j.jbiomech.2014.10.009

Cited by 183 publications

(227 citation statements)

References 47 publications

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“…The motor neuron discharges were converted into continuous neural activations using a twitch model based on a time-history dependent recursive filter and a non-linear transfer function 48 . Experimental joint angles were used as input to a multidimensional cubic B-splines set that synthetized the OpenSim subject-specific geometry of muscle-tendon units and computed their resulting length and moment arms 49,50 . Neural activations and muscletendon unit length were used to control a Hill-type muscle model and estimate instantaneous length, contraction velocity, and force in the muscle fibers, and strain and force in the series-elastic tendon within each muscle-tendon unit 49 .…”

Section: Model-based Estimationmentioning

confidence: 99%

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

et al. 2017

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The intuitive control of upper - limb prostheses requires a man/machine interface that directly exploits biological signals. Here, we define and experimentally test an offline man/machine interface that takes advantage of the discharge timings of spinal moto r neurons. The motor - neuron behaviour is identified by deconvolution of the electrical activity of muscles reinnervated by nerves of a missing limb in patients with amputation at the shoulder or humeral level. We mapped the series of motor - neuron discharge s into control commands across multiple degrees of freedom via the offline application of direct proportional control, pattern recognition and musculoskeletal modelling. A series of experiments performed on six patients reveal that the man/machine interfac e has superior offline performance than conventional direct electromyographic control applied after targeted muscle innervation. The combination of surgical procedures, decoding and mapping into effective commands constitutes an interface with the output l ayers of the spinal cord circuitry that allows for the intuitive control of multiple degrees of freedom

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Section: Model-based Estimationmentioning

confidence: 99%

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

et al. 2017

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“…It employs a nonlinear least-squares optimization procedure to identify parameters that vary nonlinearly across subjects including: EMG-to-activation parameters, MTU optimal fibre length, tendon slack length and maximal isometric force. The open-loop formulation is then generalized into a closed-loop modelling formulation [60]. This accounts for surface EMG uncertainties that may contribute to bias the model-based joint dynamics estimates including cross-talk and filtering artefacts as well as the inability to access deeply located muscles.…”

Section: Open-loop and Closed-loop Formulations Of Emg-informed Muscumentioning

confidence: 99%

Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling

Fernandez

Heidlauf

et al. 2016

Interface Focus.

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This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models.

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“…The predicted μ values were all above 0.80 (0.81-1.0), 3.05 for load holding close to the chest, 40.9, 10.9, 7.9, and 5.4 for load holding at different heights from 90 to 180 cm indicating that computed muscle forces are much less compatible with recorded EMG for tasks in upright postures. Table 3 Predictions for the global muscle forces (right/left sides), sum of global ( F G ), local lumbar ( F L ) and total muscle forces ( F T ) on both sides, local spinal loads (compression and posterior-anterior shear forces positive in downward and anterior directions, respectively) at the L4-L5 and L5-S1 disc mid-heights, and L4-L5 intradiscal pressure (IDP) values for holding 10 Unstable 20°Stable Unstable Unstable Stable 30°Stable Stable Stable Stable 40°Stable Stable Stable Stable 50°Stable Stable Stable Stable 60°Stable Stable Stable Stable 70°Stable Stable Stable Stable 80°Stable Stable Stable Stable a The smallest eigenvalue of the Hessian matrix is equal to zero (Meta stable), smaller than zero (Unstable), or greater than zero (Stable).…”

Section: Tasks In Upright Posturesmentioning

confidence: 99%

“…Hybrid, i.e. EMG-assisted optimization (EMGAO), approaches have consequently been introduced to improve model predictions by simultaneous consideration of both EMG signals and equilibrium requirements [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of trunk muscle forces, spinal loads and stability estimated by one stability- and three EMG-assisted optimization approaches

Mohammadi

Arjmand

Shirazi‐Adl

2015

Medical Engineering & Physics

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Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization

Cited by 183 publications

References 47 publications

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling

Comparison of trunk muscle forces, spinal loads and stability estimated by one stability- and three EMG-assisted optimization approaches

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