Accurate Real-Time Feedback of Surface EMG During fMRI

2007 ◽  
Vol 97 (1) ◽  
pp. 912-920 ◽  
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.

Neuromuscular Adaptation During Skill Acquisition on a Two Degree-of-Freedom Target-Acquisition Task: Isometric Torque Production

2005 ◽  
Vol 94 (5) ◽  
pp. 3046-3057 ◽  
Author(s):  
Jonathan Shemmell ◽  
Matthew Forner ◽  
James R. Tresilian ◽  
Stephan Riek ◽  
Benjamin K. Barry ◽  
...  
Keyword(s):  
Skill Acquisition ◽  
Neural Control ◽  
Muscle Activation ◽  
Joint Torque ◽  
Acquisition Time ◽  
Neutral Position ◽  
Target Acquisition ◽  
Joint Torques ◽  
Flexion Extension ◽  
Torque Development

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.

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

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.

An EMG-Driven Forward Dynamics Model to Simulate Stance Phase of Gait

2009 ◽  
Author(s):  
Qi Shao ◽  
Kurt Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Muscle Activation ◽  
Stance Phase ◽  
Control Strategies ◽  
Neuromuscular Control ◽  
Human Movement ◽  
Joint Torque ◽  
Forward Dynamics ◽  
Activation Patterns ◽  
Joint Torques ◽  
Muscle Activation Patterns

Simulations based on forward dynamics have been used to identify the biomechanical mechanisms how human movement is generated. They used either net joint torques [1] or muscle forces [2, 3, 4] as actuators to drive forward simulation. However, very few models used EMG-based patterns to define muscle excitations [4] or were actually driven by EMGs. Muscle activation patterns vary from subject to subject and from movement to movement, and the activations depend on the control task, sometimes quite different even for the same joint angle and joint torque [5]. Using EMG as input can account for subjects’ different muscle activation patterns and help revealing the neuromuscular control strategies.

scholarly journals A hybrid functional electrical stimulation for real-time estimation of joint torque and closed-loop control of muscle activation

2016 ◽  
Vol 26 (3) ◽  
Author(s):  
Zhan Li ◽  
David Guiraud ◽  
David Andreu ◽  
Charles Fattal ◽  
Anthony Gelis ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Real Time ◽  
Functional Electrical Stimulation ◽  
Muscle Activation ◽  
Time Estimation ◽  
Joint Torque ◽  
Joint Movement ◽  
Hybrid Functional ◽  
Loop Control ◽  
Electrical Pulses

As a neuroprosthetic technique, functional electrical stimulation (FES) can restore lost motor performance of impaired patients. Through delivering electrical pulses to target muscles, the joint movement can be eventually elicited. This work presents a real-time FES system which is able to deal with two neuroprosthetic missions: one is estimating FES-induced joint torque with evoked electromyograph (eEMG), and the other is artificially controlling muscle activation with such eEMG feedback. The clinical experiment results on spinal cord injured (SCI) patients and healthy subjects show promising performance of the proposed FES system.

scholarly journals Upper-Limb Isometric Force Feasible Set: Evaluation of Joint Torque-Based Models

Biomechanics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 102-117
Author(s):  
Nasser Rezzoug ◽  
Vincent Hernandez ◽  
Philippe Gorce
Keyword(s):  
Upper Limb ◽  
Isometric Force ◽  
Joint Torque ◽  
Force Distribution ◽  
Prediction Errors ◽  
Feasible Set ◽  
Joint Torques ◽  
Motor Abilities ◽  
Capacity Evaluation ◽  
Measured Force

A force capacity evaluation for a given posture may provide better understanding of human motor abilities for applications in sport sciences, rehabilitation and ergonomics. From data on posture and maximum isometric joint torques, the upper-limb force feasible set of the hand was predicted by four models called force ellipsoid, scaled force ellipsoid, force polytope and scaled force polytope, which were compared with a measured force polytope. The volume, shape and force prediction errors were assessed. The scaled ellipsoid underestimated the maximal mean force, and the scaled polytope overestimated it. The scaled force ellipsoid underestimated the volume of the measured force distribution, whereas that of the scaled polytope was not significantly different from the measured distribution but exhibited larger variability. All the models characterized well the elongated shape of the measured force distribution. The angles between the main axes of the modelled ellipsoids and polytopes and that of the measured polytope were compared. The values ranged from 7.3° to 14.3°. Over the entire surface of the force ellipsoid, 39.7% of the points had prediction errors less than 50 N; 33.6% had errors between 50 and 100 N; and 26.8% had errors greater than 100N. For the force polytope, the percentages were 56.2%, 28.3% and 15.4%, respectively.

Learning-Based Real-Time Detection of Robot Collisions Without Joint Torque Sensors

2021 ◽  
Vol 6 (1) ◽  
pp. 103-110
Author(s):  
Kyu Min Park ◽  
Jihwan Kim ◽  
Jinhyuk Park ◽  
Frank C. Park
Keyword(s):  
Real Time ◽  
Joint Torque ◽  
Real Time Detection

scholarly journals Real-time imaging of cellular forces using optical interference

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Andrew T. Meek ◽  
Nils M. Kronenberg ◽  
Andrew Morton ◽  
Philipp Liehm ◽  
Jan Murawski ◽  
...  
Keyword(s):  
Cell Culture ◽  
Real Time ◽  
High Throughput ◽  
Mechanical Activity ◽  
Dynamic Processes ◽  
Imaging Method ◽  
Neonatal Cardiomyocytes ◽  
A Cell ◽  
Cellular Forces ◽  
Time Acquisition

AbstractImportant dynamic processes in mechanobiology remain elusive due to a lack of tools to image the small cellular forces at play with sufficient speed and throughput. Here, we introduce a fast, interference-based force imaging method that uses the illumination of an elastic deformable microcavity with two rapidly alternating wavelengths to map forces. We show real-time acquisition and processing of data, obtain images of mechanical activity while scanning across a cell culture, and investigate sub-second fluctuations of the piconewton forces exerted by macrophage podosomes. We also demonstrate force imaging of beating neonatal cardiomyocytes at 100 fps which reveals mechanical aspects of spontaneous oscillatory contraction waves in between the main contraction cycles. These examples illustrate the wider potential of our technique for monitoring cellular forces with high throughput and excellent temporal resolution.

scholarly journals Joint torque and velocity optimization for a redundant leg of quadruped robot

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773189 ◽  
Author(s):  
Taihui Zhang ◽  
Honglei An ◽  
Hongxu Ma
Keyword(s):  
Angular Velocity ◽  
Sagittal Plane ◽  
Load Capacity ◽  
Optimization Criterion ◽  
Quadruped Robot ◽  
Joint Torque ◽  
Quadratic Program ◽  
Hydraulic Actuator ◽  
Joint Torques ◽  
Physical Limitations

Hydraulic actuated quadruped robot similar to BigDog has two primary performance requirements, load capacity and walking speed, so that it is necessary to balance joint torque and joint velocity when designing the dimension of single leg and controlling its motion. On the one hand, because there are three joints per leg on sagittal plane, it is necessary to firstly optimize the distribution of torque and angular velocity of every joint on the basis of their different requirements. On the other hand, because the performance of hydraulic actuator is limited, it is significant to keep the joint torque and angular velocity in actuator physical limitations. Therefore, it is essential to balance the joint torque and angular velocity which have negative correlation under the condition of constant power of the hydraulic actuator. The main purpose of this article is to optimize the distribution of joint torques and velocity of a redundant single leg with joint physical limitations. Firstly, a modified optimization criterion combining joint torques with angular velocity that takes both support phase and flight phase into account is proposed, and then the modified optimization criterion is converted into a normal quadratic programming problem. A kind of recurrent neural network is used to solve the quadratic program problem. This method avoids tremendous matrix inversion and fits for time-varying system. The achieved optimized distribution of joint torques and velocity is useful for aiding mechanical design and the following motion control. Simulation results presented in this article confirm the efficiency of this optimization algorithm.

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