A Real-Time, 3-D Musculoskeletal Model for Dynamic Simulation of Arm Movements

Chadwick, Edward K.; Blana, Dimitra; Bogert, Antonie J. van den; Kirsch, Robert F.

doi:10.1109/tbme.2008.2005946

Cited by 88 publications

(81 citation statements)

References 26 publications

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“…Triceps and anterior deltoid are hence selected for stimulation to align with the clinical need to increase muscle tone and restore motor control of weakened muscles. The relationship between muscle stimulation and subsequent movement is well explored, and sophisticated muscle models exist with multiple attachment points across more than one joint, and movement over complex sliding surfaces [25]. However, simplification opens up routes for both parameter identification and controller derivation that have not yet been possible for more complex models [10].…”

Section: B Muscle Selection and Modelingmentioning

confidence: 99%

Upper Limb Electrical Stimulation Using Input-Output Linearization and Iterative Learning Control

Freeman

2015

IEEE Trans. Contr. Syst. Technol.

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Abstract-A control scheme is developed for multi-joint upper limb reference tracking using functional electrical stimulation (FES). In accordance with the needs of stroke rehabilitation, FES is applied to a reduced set of muscles in the arm and shoulder, with support against gravity provided by a passive exoskeletal mechanism. The approach fuses input-output linearization with iterative learning control (ILC), one of the few techniques to have been applied in clinical treatment trials with patients. This powerful hybrid control structure hence extends performance and scope of clinically proven technology for widespread application in rehabilitation robotic and FES domains. In addition to simplifying tracking and convergence properties of the stimulated joints, the framework enables conditions for the stability of unstimulated joints to be derived for the first time. Experimental results confirm tracking performance of the stimulated joints, together with unstimulated joint stability.

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Section: B Muscle Selection and Modelingmentioning

confidence: 99%

Upper Limb Electrical Stimulation Using Input-Output Linearization and Iterative Learning Control

Freeman

2015

IEEE Trans. Contr. Syst. Technol.

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“…Furthermore, it is not always clear what degree of model complexity is necessary to capture muscle action. For instance, to model especially complex muscles with very broad insertions or origins, investigators often divide the muscle into several distinct regions, modeling each region with an independent insertion and origin which together span the observed muscle anatomy (Chadwick et al, 2008;Holzbaur et al, 2005). How to divide these complex muscles into distinct regions and the most appropriate location of their respective insertion and origin points is not usually obvious.…”

Section: Introductionmentioning

confidence: 99%

Estimation of musculoskeletal models from in situ measurements of muscle action in the rat hindlimb

Yeo

Mullens

Sandercock

et al. 2011

Journal of Experimental Biology

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SUMMARYMusculoskeletal models are often created by making detailed anatomical measurements of muscle properties. These measurements can then be used to determine the parameters of canonical models of muscle action. We describe here a complementary approach for developing and validating muscle models, using in situ measurements of muscle actions. We characterized the actions of two rat hindlimb muscles: the gracilis posticus (GRp) and the posterior head of biceps femoris (BFp; excluding the anterior head with vertebral origin). The GRp is a relatively simple muscle, with a circumscribed origin and insertion. The BFp is more complex, with an insertion distributed along the tibia. We measured the six-dimensional isometric forces and moments at the ankle evoked from stimulating each muscle at a range of limb configurations. The variation of forces and moments across the workspace provides a succinct characterization of muscle action. We then used this data to create a simple muscle model with a single point insertion and origin. The model parameters were optimized to best explain the observed force-moment data. This model explained the relatively simple muscle, GRp, very well (R 2 >0.85). Surprisingly, this simple model was also able to explain the action of the BFp, despite its greater complexity (R 2 >0.84). We then compared the actions observed here with those predicted using recently published anatomical measurements. Although the forces and moments predicted for the GRp were very similar to those observed here, the predictions for the BFp differed. These results show the potential utility of the approach described here for the development and refinement of musculoskeletal models based on in situ measurements of muscle actions.

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“…This is typically calculated using muscle-to-bone contact detection methods [5], [33]. The drawback is the long processing time that varies with the numbers of MTUs, DOFs, and musculoskeletal geometry complexity, thus limiting real-time Lmt and MA estimation [26], [28]. To address this problem, we integrated in our framework the MCBS method we previously developed [29] ( Fig.…”

Section: B Emg-driven Modelingmentioning

confidence: 99%

“…This all would prevent robust translation of these solutions to real-world applications. Although a real-time two-DOF upper limb model was recently proposed [28], this was not driven by voluntary EMGs but operated via functional electrical stimulation signals. Moreover, it was tested for computational speed on a desktop computer and was not validated on the ability of blindly predicting internal joint forces including multi-DOF joint moments.…”

Section: Introductionmentioning

confidence: 99%

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

Durandau

Farina

Sartori

2018

IEEE Trans. Biomed. Eng.

120

113

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Abstract-Objective: Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses. Current biomechanical models cannot be used for real-time control in man-machine interfaces. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human. Methods: We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual's anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system. Results: The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e. predicting accurate joint moments during six unseen motor tasks and about one unseen DOF. Conclusion: The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems. Significance: The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-time bio-feedback of internal mechanical forces and direct patient interfacing with wearable assistive devices.

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A Real-Time, 3-D Musculoskeletal Model for Dynamic Simulation of Arm Movements

Abstract: Abstract-Neuroprostheses

Cited by 88 publications

References 26 publications

Upper Limb Electrical Stimulation Using Input-Output Linearization and Iterative Learning Control

Upper Limb Electrical Stimulation Using Input-Output Linearization and Iterative Learning Control

Estimation of musculoskeletal models from in situ measurements of muscle action in the rat hindlimb

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

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