scholarly journals In Vivo Identification of Skeletal Muscle Dynamics with Nonlinear Kalman Filter: Comparison between EKF and SPKF

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mitsuhiro Hayashibe ◽  
David Guiraud ◽  
Philippe Poignet
Keyword(s):  
Skeletal Muscle ◽  
Kalman Filter ◽  
Initial Conditions ◽  
Medial Gastrocnemius ◽  
Muscle Model ◽  
Unknown Parameters ◽  
Muscle System ◽  
Muscle Dynamics ◽  
Subject Specific

Skeletal muscle system has nonlinear dynamics and subject-specific characteristics. Thus, it is essential to identify the unknown parameters from noisy biomedical signals to improve the modeling accuracy in neuroprosthetic control. The objective of this work is to develop an experimental identification method for subject-specific biomechanical parameters of a physiological muscle model which can be employed to predict the nonlinear force properties of stimulated muscle. Our previously proposed muscle model, which can describe multiscale physiological system based on the Hill and Huxley models, was used for the identification. The identification protocols were performed on two rabbit experiments, where the medial gastrocnemius was attached to a motorized lever system to record the force by the nerve stimulation. The muscle model was identified using nonlinear Kalman filters: sigma-point and extended Kalman filter. The identified model was evaluated by comparison with experimental measurements in the cross-validation manner. The feasibility could be demonstrated by comparison between the estimated parameter and the measured value. The estimates with SPKF showed 5.7% and 2.9% error in each experiment with 7 different initial conditions. It reveals that SPKF has great advantage especially for the identification of multiscale muscle model which accounts for the high nonlinearity and discontinuous states between muscle contraction and relaxation process.

scholarly journals Combined Ultrasound Imaging and Biomechanical Modeling to Estimate Triceps Brachii Musculotendon Changes in Stroke Survivors

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Le Li ◽  
Raymond Kai-yu Tong
Keyword(s):  
Biomechanical Model ◽  
Joint Torque ◽  
Ultrasound Measurement ◽  
Control Group ◽  
Biomechanical Modeling ◽  
Stroke Survivors ◽  
Triceps Brachii ◽  
Unknown Parameters ◽  
Subject Specific

The aim of this study was to investigate the changes of musculotendon parameters of triceps brachii in persons after stroke based on subject-specific biomechanical modeling technique combined with in vivo ultrasound measurement. Five chronic stroke survivors and five normal control subjects were recruited. B-mode ultrasound was applied to measure muscle pennation angle and the optimal length of three heads of triceps’ brachii at different joint angle positions in resting and isometric contraction. Measured ultrasound data were used to reduce the unknown parameters during the modeling optimization process. The results showed that pennation angles varied with joint angles, and the longhead TRI pennation from stroke group was smaller than the literature value. The maximum isometric muscle stress from persons after stroke was significantly smaller than that found in the unimpaired subjects. The prediction of joint torque fits well with the measured data from the control group, whereas the prediction error is larger in results from persons after stroke. In vivo parameters from ultrasound data could help to build a subject-specific biomechanical model of elbow extensor for both unimpaired and hemiplegic subjects, and then the results driven from the model could enhance the understanding of motor function changes for persons after stroke.

scholarly journals Effects of load carrying on metabolic cost and hindlimb muscle dynamics in guinea fowl (Numida meleagris)

2006 ◽  
Vol 101 (4) ◽  
pp. 1060-1069 ◽  
Author(s):  
C. P. McGowan ◽  
H. A. Duarte ◽  
J. B. Main ◽  
A. A. Biewener
Keyword(s):  
Oxygen Consumption ◽  
Muscle Activation ◽  
Guinea Fowl ◽  
Medial Gastrocnemius ◽  
Stretch Activation ◽  
Shortening Velocity ◽  
Muscle Fascicle ◽  
Muscle Dynamics ◽  
Load Carrying

The goal of this study was to test whether the contractile patterns of two major hindlimb extensors of guinea fowl are altered by load-carrying exercise. We hypothesized that changes in contractile pattern, specifically a decrease in muscle shortening velocity or enhanced stretch activation, would result in a reduction in locomotor energy cost relative to the load carried. We also anticipated that changes in kinematics would reflect underlying changes in muscle strain. Oxygen consumption, muscle activation intensity, and fascicle strain rate were measured over a range of speeds while animals ran unloaded vs. when they carried a trunk load equal to 22% of their body mass. Our results showed that loading produced no significant ( P > 0.05) changes in kinematic patterns at any speed. In vivo muscle contractile strain patterns in the iliotibialis lateralis pars postacetabularis and the medial head of the gastrocnemius showed a significant increase in active stretch early in stance ( P < 0.01), but muscle fascicle shortening velocity was not significantly affected by load carrying. The rate of oxygen consumption increased by 17% ( P < 0.01) during loaded conditions, equivalent to 77% of the relative increase in mass. Additionally, relative increases in EMG intensity (quantified as mean spike amplitude) indicated less than proportional recruitment, consistent with force enhancement via stretch activation, in the proximal iliotibialis lateralis pars postacetabularis; however, a greater than proportional increase in the medial gastrocnemius was observed. As a result, when averaged for the two muscles, EMG intensity increased in direct proportion to the fractional increase in load carried.

scholarly journals Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks

2020 ◽  
Vol 17 (162) ◽  
pp. 20190715 ◽  
Author(s):  
Ryan J. Cunningham ◽  
Ian D. Loram
Keyword(s):  
Neural Networks ◽  
Skeletal Muscle ◽  
Joint Angle ◽  
Region Of Interest ◽  
Joint Moment ◽  
Medial Gastrocnemius ◽  
Deep Convolutional Neural Networks ◽  
Non Invasive ◽  
Segmented Images

The objective is to test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal muscle states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction or generalization of independently varying active and passive states. We use deep learning to investigate the generalizable content of two-dimensional (2D) US muscle images. US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle, were recorded from 32 healthy participants (seven female; ages: 27.5, 19–65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, drift-free components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous independent variation of passive (joint angle) and active (electromyography) inputs. For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography and joint moment were estimated to accuracy 55 ± 8%, 57 ± 11% and 46 ± 9%, respectively. With 2D US imaging, deep neural networks can encode, in generalizable form, the activity–length–tension state relationship of these muscles. Observation-only, low-power 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalized muscle assessment in pain, injury, neurological conditions, neuropathies, myopathies and ageing.

scholarly journals Shifting gears: dynamic muscle shape changes and force-velocity behavior in the medial gastrocnemius

2017 ◽  
Vol 123 (6) ◽  
pp. 1433-1442 ◽  
Author(s):  
Taylor J. M. Dick ◽  
James M. Wakeling
Keyword(s):  
Skeletal Muscle ◽  
Muscle Activation ◽  
Mechanical Performance ◽  
Shape Change ◽  
Muscle Thickness ◽  
Pennation Angle ◽  
Medial Gastrocnemius ◽  
Force Velocity ◽  
Shape Changes

When muscles contract, they bulge in thickness or in width to maintain a (nearly) constant volume. These dynamic shape changes are tightly linked to the internal constraints placed on individual muscle fibers and play a key functional role in modulating the mechanical performance of skeletal muscle by increasing its range of operating velocities. Yet to date we have a limited understanding of the nature and functional implications of in vivo dynamic muscle shape change under submaximal conditions. This study determined how the in vivo changes in medial gastrocnemius (MG) fascicle velocity, pennation angle, muscle thickness, and subsequent muscle gearing varied as a function of force and velocity. To do this, we obtained recordings of MG tendon length, fascicle length, pennation angle, and thickness using B-mode ultrasound and muscle activation using surface electromyography during cycling at a range of cadences and loads. We found that that increases in contractile force were accompanied by reduced bulging in muscle thickness, reduced increases in pennation angle, and faster fascicle shortening. Although the force and velocity of a muscle contraction are inversely related due to the force-velocity effect, this study has shown how dynamic muscle shape changes are influenced by force and not influenced by velocity.NEW & NOTEWORTHY During movement, skeletal muscles contract and bulge in thickness or width. These shape changes play a key role in modulating the performance of skeletal muscle by increasing its range of operating velocities. Yet to date the underlying mechanisms associated with muscle shape change remain largely unexplored. This study identified muscle force, and not velocity, as the mechanistic driving factor to allow for muscle gearing to vary depending on the contractile conditions during human cycling.

Dynamic Optimization of Maximum-Effort Human Sprinting

2009 ◽  
Author(s):  
Ross H. Miller ◽  
Brian R. Umberger ◽  
Joseph Hamill ◽  
Graham E. Caldwell
Keyword(s):  
Skeletal Muscle ◽  
Dynamic Optimization ◽  
Human Muscle ◽  
Maximum Speed ◽  
Muscle Dynamics ◽  
Velocity Relationship ◽  
Maximum Effort ◽  
Force Velocity ◽  
Relationship Of

Maximum speed is an important parameter for sprinting humans, particularly in athletic competitions. While the biomechanics of sprinting have been well-studied [1–3], our understanding of biomechanical limits to maximum speed is still in its infancy. Previous studies have suggested a speed-limiting role for the force-velocity relationship of skeletal muscle [2], but these theories are difficult to verify experimentally due to the difficulty in observing and manipulating human muscle dynamics in vivo.

scholarly journals The Application of Deep Convolutional Neural Networks to Ultrasound for Modelling of Dynamic States within Human Skeletal Muscle

2017 ◽  
Author(s):  
Ryan J. Cunningham ◽  
Peter J. Harding ◽  
Ian D. Loram
Keyword(s):  
Skeletal Muscle ◽  
Joint Angle ◽  
Region Of Interest ◽  
Image Understanding ◽  
Joint Torque ◽  
Medial Gastrocnemius ◽  
Ultrasound Images ◽  
Deep Convolutional Neural Networks ◽  
Segmented Images

AbstractThis paper concerns the fully automatic direct in vivo measurement of active and passive dynamic skeletal muscle states using ultrasound imaging. Despite the long standing medical need (myopathies, neuropathies, pain, injury, ageing), currently technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal intramuscular states. Ultrasound provides a technology in which static and dynamic muscle states can be observed non-invasively, yet current computational image understanding approaches are inadequate. We propose a new approach in which deep learning methods are used for understanding the content of ultrasound images of muscle in terms of its measured state. Ultrasound data synchronized with electromyography of the calf muscles, with measures of joint torque/angle were recorded from 19 healthy participants (6 female, ages: 30 ± 7.7). A segmentation algorithm previously developed by our group was applied to extract a region of interest of the medial gastrocnemius. Then a deep convolutional neural network was trained to predict the measured states (joint angle/torque, electromyography) directly from the segmented images. Results revealed for the first time that active and passive muscle states can be measured directly from standard b-mode ultrasound images, accurately predicting for a held out test participant changes in the joint angle, electromyography, and torque with as little error as 0.022°, 0.0001V, 0.256Nm (root mean square error) respectively.

scholarly journals Geometric models to explore mechanisms of dynamic shape change in skeletal muscle

2018 ◽  
Vol 5 (5) ◽  
pp. 172371 ◽  
Author(s):  
Taylor J. M. Dick ◽  
James M. Wakeling
Keyword(s):  
Skeletal Muscle ◽  
Shape Change ◽  
Geometric Constraints ◽  
Muscle Performance ◽  
Medial Gastrocnemius ◽  
Type Model ◽  
Length Changes ◽  
Shape Changes ◽  
Dynamic Shape

Skeletal muscle bulges when it contracts. These three-dimensional (3D) dynamic shape changes play an important role in muscle performance by altering the range of fascicle velocities over which a muscle operates. However traditional muscle models are one-dimensional (1D) and cannot fully explain in vivo shape changes. In this study we compared medial gastrocnemius behaviour during human cycling (fascicle length changes and rotations) predicted by a traditional 1D Hill-type model and by models that incorporate two-dimensional (2D) and 3D geometric constraints to in vivo measurements from B-mode ultrasound during a range of mechanical conditions ranging from 14 to 44 N m and 80 to 140 r.p.m. We found that a 1D model predicted fascicle lengths and pennation angles similar to a 2D model that allowed the aponeurosis to stretch, and to a 3D model that allowed for aponeurosis stretch and variable shape changes to occur. This suggests that if the intent of a model is to predict fascicle behaviour alone, then the traditional 1D Hill-type model may be sufficient. Yet, we also caution that 1D models are limited in their ability to infer the mechanisms by which shape changes influence muscle mechanics. To elucidate the mechanisms governing muscle shape change, future efforts should aim to develop imaging techniques able to characterize whole muscle 3D geometry in vivo during active contractions.

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