A comparison among different Hill-type contraction dynamics formulations for muscle force estimation

Romero, Francisco; Sánchez, Francisco Javier Alonso

doi:10.5194/ms-7-19-2016

Cited by 64 publications

(33 citation statements)

References 24 publications

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“…Methods for extracting force from EMG signals can be classified into two categories: parametric model-based algorithms and nonparametric algorithms [ 15 ]. The parametric model-based algorithms [ 16 , 17 ] require accurate parameters of muscle or an accurate muscle-based model, so their applications in some important fields are restricted [ 15 ]. Therefore, nonparametric algorithms have gained increasing attention as they do not require prior knowledge of the parameters of arm muscle models.…”

Section: Related Workmentioning

confidence: 99%

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

Chen

Huang

et al. 2020

Sensors

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The precise application of tightening torque is one of the important measures to ensure accurate bolt connection and improvement in product assembly quality. Currently, due to the limited assembly space and efficiency, a wrench without the function of torque measurement is still an extensively used assembly tool. Therefore, wrench torque monitoring is one of the urgent problems that needs to be solved. This study proposes a multi-segmentation parallel convolution neural network (MSP-CNN) model for estimating assembly torque using surface electromyography (sEMG) signals, which is a method of torque monitoring through classification methods. The MSP-CNN model contains two independent CNN models with different or offset torque granularities, and their outputs are fused to obtain a finer classification granularity, thus improving the accuracy of torque estimation. First, a bolt tightening test bench is established to collect sEMG signals and tightening torque signals generated when the operator tightens various bolts using a wrench. Second, the sEMG and torque signals are preprocessed to generate the sEMG signal graphs. The range of the torque transducer is divided into several equal subdivision ranges according to different or offset granularities, and each subdivision range is used as a torque label for each torque signal. Then, the training set, verification set, and test set are established for torque monitoring to train the MSP-CNN model. The effects of different signal preprocessing methods, torque subdivision granularities, and pooling methods on the recognition accuracy and torque monitoring accuracy of a single CNN network are compared experimentally. The results show that compared to maximum pooling, average pooling can improve the accuracy of CNN torque classification and recognition. Moreover, the MSP-CNN model can improve the accuracy of torque monitoring as well as solve the problems of non-convergence and slow convergence of independent CNN network models.

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Section: Related Workmentioning

confidence: 99%

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

Chen

Huang

et al. 2020

Sensors

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“…In insects, where muscles are often innervated by very few motoneurons and single twitches can last very long, higher-order nonlinearities are used (Zakotnik et al, 2006; Wilson et al, 2013; Harischandra et al, 2019). The contraction dynamics represents the influence of muscle length and shortening velocity on the active force generation of a muscle (Hill, 1938; Aubert, 1956; Zajac, 1989; Romero and Alonso, 2016). Activation dynamics and contraction dynamics are assumed to be independent of each other although this has been discussed controversially (Rack and Westbury, 1969).…”

Section: Muscles and Compliant Actuationmentioning

confidence: 99%

Integrative Biomimetics of Autonomous Hexapedal Locomotion

et al. 2019

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Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots—ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model—we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.

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“…Several mathematical models have been proposed to represent muscle dynamics, which are usually classified into three groups [16]. The first one is based on black box approximation where the inputs are either the neural signal or the external load, and the output corresponds to either the joint position or torque.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Delayed Central Nervous System Action in the Regulation of a Third-order Muscle-Tendon Model

Ati

Boussaada

Niculescu

et al. 2020

2020 24th International Conference on System Theory, Control and Computing (ICSTCC)

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A comparison among different Hill-type contraction dynamics formulations for muscle force estimation

Cited by 64 publications

References 24 publications

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

Integrative Biomimetics of Autonomous Hexapedal Locomotion

Analysis of the Delayed Central Nervous System Action in the Regulation of a Third-order Muscle-Tendon Model

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