Scalability of the Muscular Action in a Parametric 3D Model of the Index Finger

Sancho-Bru, Joaquı́n L.; Vergara, Margarita; Rodríguez-Cervantes, Pablo-Jesús; Giurintano, David J.; Pérez-González, Antonio

doi:10.1007/s10439-007-9395-6

Cited by 15 publications

(8 citation statements)

References 16 publications

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“…The PCSA values used here might have been underestimated because they were adapted from elderly cadaver specimens. Besides, the scaling relationship was only based on subject's hand size (29) and was the same for all muscles. As for PCSA, the maximum muscle stress value could be different for every person, but to our knowledge, no studies investigated the individualization of these parameters according to individual training, health, gender, or age.…”

Section: Discussionmentioning

confidence: 99%

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Quantification of Hand and Forearm Muscle Forces during a Maximal Power Grip Task

Monsabert

Rossi

Berton

et al. 2012

Medicine &Amp; Science in Sports &Amp; Exercise

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Section: Discussionmentioning

confidence: 99%

“…PCSA", is the physiological crosssectional area of the m muscle. For the five fingers, the PCSA were taken from the study of Chao et al (8) and scaled for each subject using methods described by Sancho-Bru et al (29). Data from Ramsay et al (25) were used for wrist muscle PCSA.…”

Section: Resolutionmentioning

confidence: 99%

Quantification of Hand and Forearm Muscle Forces during a Maximal Power Grip Task

Monsabert

Rossi

Berton

et al. 2012

Medicine &Amp; Science in Sports &Amp; Exercise

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show abstract

“…Grip force was measured one hand at a time using an isometric dynamometer with standard Jamar design, and it's handle set in the second of the five discrete grip diameter adjustments possible (G200; Biometrics Ltd, Cwmfelinfach, Gwent, UK; Sancho-Bru et al, 2008). The order in which left and right hands were tested was counterbalanced across subjects.…”

Section: Methodsmentioning

confidence: 99%

Complementary roles of different oscillatory activities in the subthalamic nucleus in coding motor effort in Parkinsonism

Tan

Pogosyan

Anzak

et al. 2013

Experimental Neurology

100

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The basal ganglia may play an important role in the control of motor scaling or effort. Recently local field potential (LFP) recordings from patients with deep brain stimulation electrodes in the basal ganglia have suggested that local increases in the synchronisation of neurons in the gamma frequency band may correlate with force or effort. Whether this feature uniquely codes for effort and whether such a coding mechanism holds true over a range of efforts is unclear. Here we investigated the relationship between frequency-specific oscillatory activities in the subthalamic nucleus (STN) and manual grips made with different efforts. The latter were self-rated using the 10 level Borg scale ranging from 0 (no effort) to 10 (maximal effort). STN LFP activities were recorded in patients with Parkinson's Disease (PD) who had undergone functional surgery. Patients were studied while motor performance was improved by dopaminergic medication. In line with previous studies we observed power increase in the theta/alpha band (4–12 Hz), power suppression in the beta band (13–30 Hz) and power increase in the gamma band (55–90 Hz) and high frequency band (101–375 Hz) during voluntary grips. Beta suppression deepened, and then reached a floor level as effort increased. Conversely, gamma and high frequency power increases were enhanced during grips made with greater effort. Multiple regression models incorporating the four different spectral changes confirmed that the modulation of power in the beta band was the only independent predictor of effort during grips made with efforts rated < 5. In contrast, increases in gamma band activity were the only independent predictor of effort during grips made with efforts ≥ 5. Accordingly, the difference between power changes in the gamma and beta bands correlated with effort across all effort levels. These findings suggest complementary roles for changes in beta and gamma band activities in the STN in motor effort coding. The latter function is thought to be impaired in untreated PD where task-related reactivity in these two bands is deficient.

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“…Along these lines, the present work proposes the use of ANNs in combination with a grasping algorithm to predict the grasping posture and contact information required by an already validated 3D scalable biomechanical model of the human hand developed by the authors in previous works [14][15][16][17]. ANNs enhance the model capabilities, as they provide estimated data that substitutes for the experimental input required by the grasping algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hand Posture Prediction Using Neural Networks within a Biomechanical Model

Mora

Sancho-Bru

Pérez-González

2012

International Journal of Advanced Robotic Systems

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This paper proposes the use of artificial neural networks (ANNs) in the framework of a biomechanical hand model for grasping. ANNs enhance the model capabilities as they substitute estimated data for the experimental inputs required by the grasping algorithm used. These inputs are the tentative grasping posture and the most open posture during grasping. As a consequence, more realistic grasping postures are predicted by the grasping algorithm, along with the contact information required by the dynamic biomechanical model (contact points and normals). Several neural network architectures are tested and compared in terms of prediction errors, leading to encouraging results. The performance of the overall proposal is also shown through simulation, where a grasping experiment is replicated and compared to the real grasping data collected by a data glove device.

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Scalability of the Muscular Action in a Parametric 3D Model of the Index Finger

Abstract: A method for scaling the muscle action is proposed and used to achieve a 3D inverse dynamic model of the human finger with all its components scalable. This method is based on scaling the PCSA

Cited by 15 publications

References 16 publications

Quantification of Hand and Forearm Muscle Forces during a Maximal Power Grip Task

Quantification of Hand and Forearm Muscle Forces during a Maximal Power Grip Task

Complementary roles of different oscillatory activities in the subthalamic nucleus in coding motor effort in Parkinsonism

Hand Posture Prediction Using Neural Networks within a Biomechanical Model

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