Sensitivity analysis guided improvement of an electromyogram-driven lumped parameter musculoskeletal hand model

Hinson, Robert; Saul, Katherine R.; Kamper, Derek G.; Huang, He

doi:10.1016/j.jbiomech.2022.111200

Cited by 2 publications

(3 citation statements)

References 43 publications

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“…Aside from the evaluation of the grid densities of the DC method, we further explore the impact of the sample frequencies on the baseline methods, according to [20], [42]. In this experiment, we estimate the optimised MSK parameters with the optimisation trial with 20 Hz, 50 Hz, 100 Hz, 200 Hz, 500 Hz, and 1000 Hz sample frequencies, via GA, SA, and PSO respectively.…”

Section: B Effects Of Grid Densitymentioning

confidence: 99%

“…The subject-specific MSK models are obtained through the objective functions that best match the experimental and estimated motion variables. Due to a large number of physiological parameters in the MSK model, the evolutionary algorithms are commonly employed such as the particle swarm optimisation (PSO) [16], [17], generic algorithm (GA) [18], [19], simulated annealing algorithm (SA) [20], and gravitational search algorithm (GSO) [21]. Nevertheless, it emerges that EMG-driven MSK models achieve the high estimation accuracy based on the expense of rapidity.…”

Section: Introductionmentioning

confidence: 99%

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Computationally Efficient Personalized EMG-Driven Musculoskeletal Model of Wrist Joint

Zhao

Zhang

et al. 2023

IEEE Trans. Instrum. Meas.

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Myoelectric control has gained much attention which translates the human intentions into control commands for exoskeletons. The electromyogram (EMG)-driven musculoskeletal (MSK) model shows prominent performance given its ability to interpret the underlying neuromechanical processes among the musculoskeletal system. This model-based scheme contains inherent physiological parameters, e.g., isometric muscle force, tendon slack length, or optimal muscle fibre length, which need to be tailored for each individual via minimising the differences between the experimental measurement and model estimation. However, the creation of the personalised EMGdriven MSK model through the evolutionary algorithms is timeconsuming, hurdling the use of the EMG-driven MSK model in practical scenarios. This paper proposes a computational efficient optimisation method to estimate the subject-specific physiological parameters for a wrist MSK model based on the direct collocation method. By constraining control variables to the experimentally measured EMG signals and introducing the physiological parameters into control variables, fast optimisation is achieved by identifying the discretised parameters at each grid simultaneously. Experimental evaluations on 12 healthy subjects are performed. Results demonstrate the proposed method outperforms the baseline optimisation algorithms used in the literature, including genetic algorithm, simulated annealing algorithm, and particle swarm optimisation algorithm. The proposed direct collocation method shows the possibility to alleviate the costly optimisation procedure and facilitate the use of the MSK model in practical applications.

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Section: B Effects Of Grid Densitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Personalized EMG-Driven Musculoskeletal Model of Wrist Joint

Zhao

Zhang

et al. 2023

IEEE Trans. Instrum. Meas.

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“…Model performance and optimization time were computed based on the number of parameters used in optimization. Paired t-tests to outcomes with full parameter optimization were conducted to evaluate differences in performance with model refinement [ 36 ]. Simulations were processed on a desktop (Intel i7-9700, 24 GB RAM).…”

Section: Ethodsmentioning

confidence: 99%

Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation

Rubin,

Hinson,

Saul

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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There has been increased interest in using residual muscle activity for neural control of powered lower-limb prostheses. However, only surface electromyography (EMG)-based decoders have been investigated. This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without amputation (NON) and seven people with amputation (AMP) participated in the experiments. Subjects conducted isometric dorsi- and plantarflexion with their intact limb by tracing desired muscle activity of the tibialis anterior (TA) and gastrocnemius (GA) while ankle torque was recorded. To match phantom limb and intact limb activity, AMP mirrored muscle activation with their residual TA and GA. We compared neuromuscular decoders (linear regression) for ankle joint torque estimation based on 1) EMG amplitude (aEMG), 2) Mu firing frequencies representing neural drive (ND), and 3) MU firings convolved with modeled twitch forces (MUDrive). In addition, sensitivity analysis and dimensionality reduction of optimization were performed on the MUDrive method to further improve its practical value. Our results suggest MUDrive significantly outperforms (lower root-mean-square error) EMG and ND methods in muscles of NON, as well as both intact and residual muscles of AMP. Reducing the number of optimized MUDrive parameters degraded performance. Even so, optimization computational time was reduced and MUDrive still outperformed aEMG. Our outcomes indicate integrating MU discharges with modeled biomechanical outputs may provide a more accurate torque control signal than direct EMG control of assistive, lower-limb devices, such as exoskeletons and powered prostheses.

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Sensitivity analysis guided improvement of an electromyogram-driven lumped parameter musculoskeletal hand model

Cited by 2 publications

References 43 publications

Computationally Efficient Personalized EMG-Driven Musculoskeletal Model of Wrist Joint

Computationally Efficient Personalized EMG-Driven Musculoskeletal Model of Wrist Joint

Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation

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