EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment about Multiple Degrees of Freedom in the Human Lower Extremity

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

doi:10.1371/journal.pone.0052618

Cited by 263 publications

(355 citation statements)

References 43 publications

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“…This inconsistency may explain why EMG-driven models may not offer a solution when several EMGs are input to the model [19]. Other authors optimized the model parameters within physiological boundaries, solving the model consistency problem and ensuring that a solution to the motion problem exists [18,20,46]. However, the optimized model probably provides little information about how the calculated solution represents the subject under study because of the typically large variability of physiological parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Inverse EMG-driven models solve the muscle load-sharing problem by forcing the static optimization solution within an arbitrarily defined interval around the calculated EMGdriven muscle force to ensure that a solution to the problem exists [19]. Forward EMG-driven models use optimizationbased procedures to tune the model, so that the calculated EMG-driven muscle forces generate the desired motion of the model [18,20]. Lloyd & Besier [18] used an EMG-driven model of the knee-spanning muscles to estimate the knee torque.…”

Section: Introductionmentioning

confidence: 99%

“…Lloyd & Besier [18] used an EMG-driven model of the knee-spanning muscles to estimate the knee torque. Sartori et al [20] used a lower-limb model to calculate muscle forces during walking, running, sidestepping and crossover cutting manoeuvres. However, the single representative muscle synergy calculated using both EMG-driven and static optimization methods cannot provide information about the spectrum of muscle synergies driving motion [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic modelling of muscle recruitment during activity

et al. 2015

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One contribution of 11 to a theme issue 'Multiscale modelling in biomechanics: theoretical, computational and translational challenges'. Muscle forces can be selected from a space of muscle recruitment strategies that produce stable motion and variable muscle and joint forces. However, current optimization methods provide only a single muscle recruitment strategy. We modelled the spectrum of muscle recruitment strategies while walking. The equilibrium equations at the joints, muscle constraints, static optimization solutions and 15-channel electromyography (EMG) recordings for seven walking cycles were taken from earlier studies. The spectrum of muscle forces was calculated using Bayesian statistics and Markov chain Monte Carlo (MCMC) methods, whereas EMG-driven muscle forces were calculated using EMG-driven modelling. We calculated the differences between the spectrum and EMG-driven muscle force for 1-15 input EMGs, and we identified the muscle strategy that best matched the recorded EMG pattern. The best-fit strategy, static optimization solution and EMG-driven force data were compared using correlation analysis. Possible and plausible muscle forces were defined as within physiological boundaries and within EMG boundaries. Possible muscle and joint forces were calculated by constraining the muscle forces between zero and the peak muscle force. Plausible muscle forces were constrained within six selected EMG boundaries. The spectrum to EMG-driven force difference increased from 40 to 108 N for 1-15 EMG inputs. The best-fit muscle strategy better described the EMG-driven pattern (R 2 ¼ 0.94; RMSE ¼ 19 N) than the static optimization solution (R 2 ¼ 0.38; RMSE ¼ 61 N). Possible forces for 27 of 34 muscles varied between zero and the peak muscle force, inducing a peak hip force of 11.3 body-weights. Plausible muscle forces closely matched the selected EMG patterns; no effect of the EMG constraint was observed on the remaining muscle force ranges. The model can be used to study alternative muscle recruitment strategies in both physiological and pathophysiological neuromotor conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic modelling of muscle recruitment during activity

et al. 2015

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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%

“…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 . The computed forces were projected onto all upper extremity degrees of freedom simultaneously via the moment arms.…”

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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