Forward and inverse dynamics modeling of human shoulder-arm musculoskeletal system with scapulothoracic constraint

Hu, Tingli; Kühn, Johannes; Haddadin, Sami

doi:10.1080/10255842.2020.1764945

Cited by 10 publications

(9 citation statements)

References 40 publications

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“…With more elegant design of (t), for example, muscle activation measured by electromyography (EMG) could be used for the intent force modeling. A recent comprehensive attempt at human arm dynamics modeling has been proposed in [33]. By modeling the motion dynamics of the arm and using time-variant muscle activation measured by EMG as the time-variant part, the human arm can be modeled similar to any multibody series manipulator.…”

Section: Human Operatormentioning

confidence: 99%

Force-Sensor-Less Bilateral Teleoperation Control of Dissimilar Master–Slave System With Arbitrary Scaling

Lampinen

Koivumäki

Zhu

et al. 2022

IEEE Trans. Contr. Syst. Technol.

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This study designs a high-precision bilateral teleoperation control for a dissimilar master-slave system. The proposed nonlinear control design takes advantage of a novel subsystem-dynamics-based control method that allows designing of individual (decentralized) model-based controllers for the manipulators locally at the subsystem level. Very importantly, a dynamic model of the human operator is incorporated into the control of the master manipulator. The individual controllers for the dissimilar master and slave manipulators are connected in a specific communication channel for the bilateral teleoperation to function. Stability of the overall control design is rigorously guaranteed with arbitrary time delays. Novel features of this study include the completely force-sensor-less design for the teleoperation system with a solution for a uniquely introduced computational algebraic loop, a method of estimating the exogenous operating force of an operator and the use of a commercial haptic manipulator. Most importantly, we conduct experiments on a dissimilar system in two degrees of freedom (DOFs). As an illustration of the performance of the proposed system, a force scaling factor of up to 800 and position scaling factor of up to 4 was used in the experiments. The experimental results show an exceptional tracking performance, verifying the real-world performance of the proposed concept.

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Section: Human Operatormentioning

confidence: 99%

Force-Sensor-Less Bilateral Teleoperation Control of Dissimilar Master–Slave System With Arbitrary Scaling

Lampinen

Koivumäki

Zhu

et al. 2022

IEEE Trans. Contr. Syst. Technol.

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“…In order to perform the sensitivity analysis, a Hill-type muscle model was constructed based on Eqs. 1, 2, 3.1, 4.1, and 5 with force curve parameters from Hu et al [11]. Isometric forces were calculated within the normalized fiber length range of 0.35-1.50 with maximal activation and a rigid tendon.…”

Section: A Conventional Sensitivity Analysismentioning

confidence: 99%

Musculotendon Parameters in Lower Limb Models: Simplifications, Uncertainties, and Muscle Force Estimation Sensitivity

Chen

Franklin

2022

Preprint

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Objective: Musculotendon parameters are key factors in the Hill-type muscle contraction dynamics, determining the muscle force estimation accuracy of a musculoskeletal model. Their values are mostly derived from muscle architecture datasets, whose emergence has been a major impetus for model development. However, it is often not clear if such parameter update indeed improves simulation accuracy. Our goal is to explain to model users in which way and how accurate these parameters are derived, and to what extent errors in parameter values might influence force estimation. Methods: We examine in detail the derivation of musculotendon parameters in six muscle architecture datasets and four prominent OpenSim models of the lower limb, and then identify simplifications which could add uncertainties to the derived parameter values. Finally, we analyze the sensitivity of muscle force estimation to these parameters both numerically and analytically. Results: Nine typical simplifications in parameter derivation are identified. Partial derivatives of the Hill-type contraction dynamics are derived. Tendon slack length is determined as the musculotendon parameter that muscle force estimation is most sensitive to, whereas pennation angle is the least impactful. Conclusion: Anatomical measurements alone are not enough to calibrate musculotendon parameters, and the improvement on muscle force estimation accuracy will be limited if the source muscle architecture datasets are the only main update. Significance: Model users may check if a dataset or model is free of concerning factors for their research or application requirements. The derived partial derivatives may be used as gradients for musculotendon parameter calibration. For model development, we demonstrate that it is more promising to focus on other model parameters or components and seek alternative strategies to further increase simulation accuracy.

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“…Before each acquisition, the recorded data were carefully visually checked on-line by an expert experimenter, and sensor locations were adjusted if necessary. Part of these data were successfully used for the identification of task-dependent muscle synergies in [ 33 ] and for the validation of a human shoulder-arm musculoskeletal dynamic model in [ 36 ], to which the interested reader is referred for an example on how to pre-process and analyze the data. It is worth mentioning that in the literature EMG data typically undergo a number of pre-processing steps to increase the quality of the collected signal and make it usable for further analyses.…”

Section: Analyses and Technical Validationmentioning

confidence: 99%

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

et al. 2021

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Background Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. Contribution This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.

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Forward and inverse dynamics modeling of human shoulder-arm musculoskeletal system with scapulothoracic constraint

Cited by 10 publications

References 40 publications

Force-Sensor-Less Bilateral Teleoperation Control of Dissimilar Master–Slave System With Arbitrary Scaling

Force-Sensor-Less Bilateral Teleoperation Control of Dissimilar Master–Slave System With Arbitrary Scaling

Musculotendon Parameters in Lower Limb Models: Simplifications, Uncertainties, and Muscle Force Estimation Sensitivity

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

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