Impact of finger posture on mapping from muscle activation to joint torque

In this study we attempted to identify the principles that govern the changes in neural control that occur during repeated performance of a multiarticular coordination task. Eight participants produced isometric flexion/extension and pronation/supination torques at the radiohumeral joint, either in isolation (e.g., flexion) or in combination (e.g., flexion–supination), to acquire targets presented by a visual display. A cursor superimposed on the display provided feedback of the applied torques. During pre- and postpractice tests, the participants acquired targets in eight directions located either 3.6 cm (20% maximal voluntary contraction [MVC]) or 7.2 cm (40% MVC) from a neutral cursor position. On each of five consecutive days of practice the participants acquired targets located 5.4 cm (30% MVC) from the neutral position. EMG was recorded from eight muscles contributing to torque production about the radiohumeral joint during the pre- and posttests. Target-acquisition time decreased significantly with practice in most target directions and at both target torque levels. These performance improvements were primarily associated with increases in the peak rate of torque development after practice. At a muscular level, these changes were brought about by increases in the rates of recruitment of all agonist muscles. The spatiotemporal organization of muscle synergies was not significantly altered after practice. The observed adaptations appear to lead to performances that are generalizable to actions that require both greater and smaller joint torques than that practiced, and may be successfully recalled after a substantial period without practice. These results suggest that tasks in which performance is improved by increasing the rate of muscle activation, and thus the rate of joint torque development, may benefit in terms of the extent to which acquired levels of performance are maintained over time.

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Accurate Real-Time Feedback of Surface EMG During fMRI

Journal of Neurophysiology ◽

10.1152/jn.00679.2006 ◽

2007 ◽

Vol 97 (1) ◽

pp. 912-920 ◽

Cited By ~ 12

Author(s):

G. Ganesh ◽

D. W. Franklin ◽

R. Gassert ◽

H. Imamizu ◽

M. Kawato

Keyword(s):

Real Time ◽

Muscle Activation ◽

Noise Removal ◽

Surface Emg ◽

Joint Torque ◽

Good Resolution ◽

Muscle System ◽

Joint Torques ◽

Individual Muscle ◽

Time Acquisition

Real-time acquisition of EMG during functional MRI (fMRI) provides a novel method of controlling motor experiments in the scanner using feedback of EMG. Because of the redundancy in the human muscle system, this is not possible from recordings of joint torque and kinematics alone, because these provide no information about individual muscle activation. This is particularly critical during brain imaging because brain activations are not only related to joint torques and kinematics but are also related to individual muscle activation. However, EMG collected during imaging is corrupted by large artifacts induced by the varying magnetic fields and radio frequency (RF) pulses in the scanner. Methods proposed in literature for artifact removal are complex, computationally expensive, and difficult to implement for real-time noise removal. We describe an acquisition system and algorithm that enables real-time acquisition for the first time. The algorithm removes particular frequencies from the EMG spectrum in which the noise is concentrated. Although this decreases the power content of the EMG, this method provides excellent estimates of EMG with good resolution. Comparisons show that the cleaned EMG obtained with the algorithm is, like actual EMG, very well correlated with joint torque and can thus be used for real-time visual feedback during functional studies.

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Estimation of isometric joint torque from muscle activation and length in intrinsic hand muscle

2008 International Conference on Control, Automation and Systems ◽

10.1109/iccas.2008.4694273 ◽

2008 ◽

Author(s):

Won-Il Park ◽

Hae-Dong Lee ◽

Jung Kim

Keyword(s):

Muscle Activation ◽

Joint Torque ◽

Intrinsic Hand Muscle ◽

Hand Muscle

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An Investigation of the Relationship between Hip Extension Torque, Hip Extension Velocity, and Muscle Activation

Journal of Applied Biomechanics ◽

10.1123/jab.15.3.253 ◽

1999 ◽

Vol 15 (3) ◽

pp. 253-269

Author(s):

David Hawkins ◽

Mark Smeulders

Keyword(s):

Muscle Activation ◽

Isometric Force ◽

Joint Torque ◽

Type Model ◽

Entire Group ◽

Model Parameters ◽

Hill Model ◽

Effort Level ◽

Isometric Effort ◽

Hip Extension

The purpose of this study was to determine if the characteristic Hill model, used to describe me force–velocity relationship for isolated tetanically stimulated muscle, could be modified and used to describe me torque–velocity behavior of me hip for maximally and submaximally stimulated hip extensor muscles. Fourteen subjects performed hip extension movements at effort levels of 100%, 70%, and 40% of a maximum isometric effort. A solenoid provided isometric resistance to hip extension. Once the desired effort level was achieved, as indicated by me isometric force, the solenoid released and me hip moved against an opposing elastic resistance equal to 75%, 50%, 25%, and 0% of the specified effort level. An electrogoniometer quantified hip angle. Hip velocity was determined by numerically differentiating the angle data. Torque-velocity-activation (or effort level) data were determined for each trial. Model parameters were determined to give me best fit to the data for each subject. Average parameter values were determined for each gender and for the entire group. The modified Hill-type model, Tm = (Tmax · A − K1 · ω)/(K2 · ω + 1), accurately describes me relationship between joint torque (Tm), maximum isometric joint torque (Tmax), joint velocity (ω), and muscle activation level (A) for subject-specific parameters (K1 and K2), but not for parameters averaged across genders or the entire group. Values for Tmax, K1, and K2 ranged from 90 to 385 Nm, 6.1 to 47.9 Nms, and 0.030 to 0.716 s, respectively.

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Organizing principles for single-joint movements. II. A speed-sensitive strategy

Journal of Neurophysiology ◽

10.1152/jn.1989.62.2.358 ◽

1989 ◽

Vol 62 (2) ◽

pp. 358-368 ◽

Cited By ~ 136

Author(s):

D. M. Corcos ◽

G. L. Gottlieb ◽

G. C. Agarwal

Keyword(s):

Muscle Activation ◽

Human Subjects ◽

Movement Time ◽

Initial Position ◽

Joint Torque ◽

Movement Speed ◽

Movement Kinematics ◽

Antagonist Muscles ◽

Speed Accuracy ◽

Organizing Principles

1. Normal human subjects made discrete flexions of the elbow over a fixed distance in the horizontal plane from a stationary initial position to a visually defined target. We measured joint angle, acceleration, and electromyograms (EMGs) from two agonist and two antagonist muscles. 2. Changes in movement speed were elicited either by explicit instruction to the subject or by adjusting the target width. Instructions always required accurately stopping in the target zone. 3. Peak inertial torques and accelerations, movement times, and integrated EMGs were all highly correlated with speed. We show that inertial torque can be used as a linking variable that is almost sufficient to explain all correlations between the task, the EMG, and movement kinematics. 4. When subjects perform tasks that require control of movement speed, they adjust the rate at which torque is developed by the muscles. This rate is modulated by the way in which the muscles are activated. The rate at which joint torque develops is correlated with the rate at which the agonist EMG rises as well as with integrated EMG. 5. The antagonist EMG shows two components. The latency of the first is 30-50 ms and independent of movement dynamics. The latency of the second component is proportional to movement time. The rate of rise and area of both components scale with torque. 6. We propose organizing principles for the control of single-joint movements in which tasks are performed by one of two strategies. These are called speed-insensitive and speed-sensitive strategies. 7. A model is proposed in which movements made under a speed-sensitive strategy are executed by controlling the intensity of an excitation pulse delivered to the motoneuron pool. The effect is to regulate the rate at which joint torque, and consequently acceleration, increases. 8. Movements of variable distance, speed, accuracy, and load are shown to be controlled by one of two consistent sets of rules for muscle activation. These rules apply to the control of both the agonist and antagonist muscles. Rules of activation lead to distinguishable patterns of EMG and torque development. All observable changes in movement kinematics are explained as deterministic consequences of these effects.

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ESTIMATING MUSCLE FORCES AND KNEE JOINT TORQUE USING SURFACE ELECTROMYOGRAPHY: A MUSCULOSKELETAL BIOMECHANICAL MODEL

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519417500695 ◽

2017 ◽

Vol 17 (04) ◽

pp. 1750069 ◽

Cited By ~ 2

Author(s):

JIANGCHENG CHEN ◽

XIAODONG ZHANG ◽

LINXIA GU ◽

CARL NELSON

Keyword(s):

Knee Joint ◽

Surface Electromyography ◽

Neural Control ◽

Muscle Activation ◽

Muscle Force ◽

Dynamic Properties ◽

Biomechanical Model ◽

Joint Torque ◽

Muscle Forces ◽

Flexion Extension

Surface electromyography (sEMG) is a useful tool for revealing the underlying musculoskeletal dynamic properties in the human body movement. In this paper, a musculoskeletal biomechanical model which relates the sEMG and knee joint torque is proposed. First, the dynamic model relating sEMG to skeletal muscle activation considering frequency and amplitude is built. Second, a muscle contraction model based on sliding-filament theory is developed to reflect the physiological structure and micro mechanical properties of the muscle. The muscle force and displacement vectors are determined and the transformation from muscle force to knee joint moment is realized, and finally a genetic algorithm-based calibration method for the Newton–Euler dynamics and overall musculoskeletal biomechanical model is put forward. Following the model calibration, the flexion/extension (FE) knee joint torque of eight subjects under different walking speeds was predicted. Results show that the forward biomechanical model can capture the general shape and timing of the joint torque, with normalized mean residual error (NMRE) of [Formula: see text]10.01%, normalized root mean square error (NRMSE) of [Formula: see text]12.39% and cross-correlation coefficient of [Formula: see text]0.926. The musculoskeletal biomechanical model proposed and validated in this work could facilitate the study of neural control and how muscle forces generate and contribute to the knee joint torque during human movement.

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Relationship between Knee Joint Torque, Velocity, and Muscle Activation: Considerations for Musculoskeletal Modeling

Journal of Applied Biomechanics ◽

10.1123/jab.14.2.141 ◽

1998 ◽

Vol 14 (2) ◽

pp. 141-157 ◽

Cited By ~ 3

Author(s):

David Hawkins ◽

Mark Smeulders

Keyword(s):

Muscle Activation ◽

Joint Torque ◽

Type Model ◽

Entire Group ◽

Model Parameters ◽

Hill Model ◽

Effort Level ◽

Isometric Effort ◽

Level Data ◽

Angle Data

The purpose of this study was to determine if the Hill model, used to describe the force-velocity relationship for isolated tetanically stimulated muscle, could be modified and used to describe the torque-velocity behavior of the knee for maximally and submaximally stimulated quadriceps and hamstrings muscles. Fourteen subjects performed both knee flexion and extension movements at 100%, 70%, and 40% of maximum isometric effort. For each effort level, the knee was allowed to move against resistances equal to 75%, 50%, 25%, and 0% of the specified effort level. An electrogoniometer quantified knee angle. Knee velocity was determined by numerically differentiating the joint angle data. Torque-velocity-activation (or effort level) data were determined for each trial. Model parameters were determined to give the best fit to the data for each subject. Average parameter values were determined for each gender and for the entire group. The modified Hill-type model accurately described the relationship between torque, velocity, and muscle activation level for subject-specific parameters but not for parameters averaged across genders or the entire group.

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Transmission Comparison for Cooperative Robotic Applications

Actuators ◽

10.3390/act10090203 ◽

2021 ◽

Vol 10 (9) ◽

pp. 203

Author(s):

Mark J. Nandor ◽

Maryellen Heebner ◽

Roger Quinn ◽

Ronald J. Triolo ◽

Nathaniel S. Makowski

Keyword(s):

Muscle Activation ◽

Mechanical Efficiency ◽

Assistive Devices ◽

Joint Torque ◽

Harmonic Drive ◽

Electromechanical Actuators ◽

Passive Resistance ◽

Isometric Torque ◽

Planetary Transmission ◽

Robotic Applications

The development of powered assistive devices that integrate exoskeletal motors and muscle activation for gait restoration benefits from actuators with low backdrive torque. Such an approach enables motors to assist as needed while maximizing the joint torque muscles, contributing to movement, and facilitating ballistic motions instead of overcoming passive dynamics. Two electromechanical actuators were developed to determine the effect of two candidate transmission implementations for an exoskeletal joint. To differentiate the transmission effects, the devices utilized the same motor and similar gearing. One actuator included a commercially available harmonic drive transmission while the other incorporated a custom designed two-stage planetary transmission. Passive resistance and mechanical efficiency were determined based on isometric torque and passive resistance. The planetary-based actuator outperformed the harmonic-based actuator in all tests and would be more suitable for hybrid exoskeletons.

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InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.759489 ◽

2021 ◽

Vol 15 ◽

Author(s):

Ali Nasr ◽

Keaton A. Inkol ◽

Sydney Bell ◽

John McPhee

Keyword(s):

Machine Learning ◽

Real Time ◽

Muscle Activation ◽

Joint Angle ◽

Learning Model ◽

Joint Torque ◽

Static Optimization ◽

Machine Learning Model ◽

Joint Velocity ◽

Joint Acceleration

InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88–91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.

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