A Myokinetic Arm Model for Estimating Joint Torque and Stiffness From EMG Signals During Maintained Posture

2009 ◽  
Vol 101 (1) ◽  
pp. 387-401 ◽  
Author(s):  
Duk Shin ◽  
Jaehyo Kim ◽  
Yasuharu Koike
Keyword(s):  
Perturbation Method ◽  
Muscle Activation ◽  
Muscle Tension ◽  
Joint Stiffness ◽  
Quadratic Function ◽  
Joint Torque ◽  
Physiological Data ◽  
Muscle Properties ◽  
Human Arm ◽  
Novel Method

The perturbation method has been used to measure stiffness of the human arm with a manipulator. Results are averages of stiffness during short perturbation intervals (<0.4 s) and also vary with muscle activation. We therefore propose a novel method for estimating static arm stiffness from muscle activation without the use of perturbation. We developed a mathematical muscle model based on anatomical and physiological data to estimate joint torque solely from EMG. This model expresses muscle tension using a quadratic function of the muscle activation and parameters representing muscle properties. The parameters are acquired from the relation between EMG and measured torque. Using this model, we were able to reconstruct joint torque from EMG signals with or without co-contraction. Joint stiffness is directly obtained by differentiation of this model analytically. We confirmed that the proposed method can be used to estimate joint torque, joint stiffness, and stiffness ellipses simultaneously for various postures with the same parameters and produces results consistent with the conventional perturbation method.

Compliance of single joints: elastic and plastic characteristics

1988 ◽  
Vol 59 (3) ◽  
pp. 937-951 ◽  
Author(s):  
G. L. Gottlieb ◽  
G. C. Agarwal
Keyword(s):  
Inflection Point ◽  
Muscle Activation ◽  
Human Subjects ◽  
Equilibrium Points ◽  
Characteristic Curve ◽  
Muscle Tension ◽  
Joint Stiffness ◽  
Elastic Stiffness ◽  
Joint Torque ◽  
Equilibrium Angle

1. Step changes in torque were applied to the elbow or ankle joint of normal human subjects who exerted constant levels of effort. They were instructed to not react to the torque but to allow their limbs to move to a new equilibrium position. In this experimental paradigm, the joint may be characterized by a nonlinear compliant element. The aim of this study was to characterize the elastic properties of the compliant element. 2. Joint elasticity is described by an S-shaped relation between torque and angle (a "compliant characteristic curve"). The stiffness of a joint is greatest for small perturbations and decreases as the size of the perturbation is increased whether the limb is loaded or unloaded from its initial equilibrium. 3. The S shape of the compliant characteristic curve is relatively constant when measured at different initial joint angles from the same initial joint torque. 4. Higher levels of initial muscle torque increase the steepness of the compliant characteristic curve. 5. All changes in initial joint torque and angle preserve the S shape. The inflection point of the characteristic curve is always at the initial equilibrium angle and torque. This shifting of the inflection point of the torque-angle relation implies a fundamental plasticity in joint compliance. The elastic component is not invariant but changes with the joint's initial equilibrium state. 6. Changes in muscle tension and length that result from a perturbation are accompanied by changes in muscle activation. The relationship between perturbation torque and mean equilibrium EMG is similar to that found for voluntary isometric contraction. It is not possible to conclude what proportion of the late EMG response to perturbation is mediated by segmental reflex mechanisms. 7. At the levels of torque used here, changes in joint stiffness are highly correlated with changes in tonic contraction of the muscle opposing the load. This change in stiffness is not the result of antagonist coactivation, which was minimal. 8. The compliant characteristic curves of elbow and ankle are qualitatively similar. The principal difference is due to the greater passive stiffness of the ankle. 9. Our findings are inconsistent with aspects of the theory of invariant characteristics or with models of movement and load compensation that postulate a control scheme based only on the setting of muscle and reflex equilibrium points. The data are also incompatible with models that only control the elastic stiffness of the muscle.

Are Complex Control Signals Required for Human Arm Movement?

1998 ◽  
Vol 79 (3) ◽  
pp. 1409-1424 ◽  
Author(s):  
Paul L. Gribble ◽  
David J. Ostry ◽  
Vittorio Sanguineti ◽  
Rafael Laboissière
Keyword(s):  
Joint Stiffness ◽  
Arm Movement ◽  
Movement Speed ◽  
Muscle Properties ◽  
Complex Control ◽  
Equilibrium Trajectory ◽  
Control Signals ◽  
Human Arm ◽  
Constant Rate ◽  
Arm Motion

Gribble, Paul L., David J. Ostry, Vittorio Sanguineti, and Rafael Laboissière. Are complex control signals required for human arm movement? J. Neurophysiol. 79: 1409–1424, 1998. It has been proposed that the control signals underlying voluntary human arm movement have a “complex” nonmonotonic time-varying form, and a number of empirical findings have been offered in support of this idea. In this paper, we address three such findings using a model of two-joint arm motion based on the λ version of the equilibrium-point hypothesis. The model includes six one- and two-joint muscles, reflexes, modeled control signals, muscle properties, and limb dynamics. First, we address the claim that “complex” equilibrium trajectories are required to account for nonmonotonic joint impedance patterns observed during multijoint movement. Using constant-rate shifts in the neurally specified equilibrium of the limb and constant cocontraction commands, we obtain patterns of predicted joint stiffness during simulated multijoint movements that match the nonmonotonic patterns reported empirically. We then use the algorithm proposed by Gomi and Kawato to compute a hypothetical equilibrium trajectory from simulated stiffness, viscosity, and limb kinematics. Like that reported by Gomi and Kawato, the resulting trajectory was nonmonotonic, first leading then lagging the position of the limb. Second, we address the claim that high levels of stiffness are required to generate rapid single-joint movements when simple equilibrium shifts are used. We compare empirical measurements of stiffness during rapid single-joint movements with the predicted stiffness of movements generated using constant-rate equilibrium shifts and constant cocontraction commands. Single-joint movements are simulated at a number of speeds, and the procedure used by Bennett to estimate stiffness is followed. We show that when the magnitude of the cocontraction command is scaled in proportion to movement speed, simulated joint stiffness varies with movement speed in a manner comparable with that reported by Bennett. Third, we address the related claim that nonmonotonic equilibrium shifts are required to generate rapid single-joint movements. Using constant-rate equilibrium shifts and constant cocontraction commands, rapid single-joint movements are simulated in the presence of external torques. We use the procedure reported by Latash and Gottlieb to compute hypothetical equilibrium trajectories from simulated torque and angle measurements during movement. As in Latash and Gottlieb, a nonmonotonic function is obtained even though the control signals used in the simulations are constant-rate changes in the equilibrium position of the limb. Differences between the “simple” equilibrium trajectory proposed in the present paper and those that are derived from the procedures used by Gomi and Kawato and Latash and Gottlieb arise from their use of simplified models of force generation.

scholarly journals Multijoint Muscle Regulation Mechanisms Examined by Measured Human Arm Stiffness and EMG Signals

1999 ◽  
Vol 81 (4) ◽  
pp. 1458-1468 ◽  
Author(s):  
Rieko Osu ◽  
Hiroaki Gomi
Keyword(s):  
Force Control ◽  
Elbow Joint ◽  
Muscle Activation ◽  
Joint Stiffness ◽  
Feedback Gain ◽  
Static Force ◽  
Muscle Regulation ◽  
Biarticular Muscles ◽  
Human Arm ◽  
Regulation Mechanisms

Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals. Stiffness properties of the musculo-skeletal system can be controlled by regulating muscle activation and neural feedback gain. To understand the regulation of multijoint stiffness, we examined the relationship between human arm joint stiffness and muscle activation during static force control in the horizontal plane by means of surface electromyographic (EMG) studies. Subjects were asked to produce a specified force in a specified direction without cocontraction or they were asked to keep different cocontractions while producing or not producing an external force. The stiffness components of shoulder, elbow, and their cross-term and the EMG of six related muscles were measured during the tasks. Assuming that the EMG reflects the corresponding muscle stiffness, the joint stiffness was predicted from the EMG by using a two-link six-muscle arm model and a constrained least-square-error regression method. Using the parameters estimated in this regression, single-joint stiffness (diagonal terms of the joint-stiffness matrix) was decomposed successfully into biarticular and monoarticular muscle components. Although biarticular muscles act on both shoulder and elbow, they were found to covary strongly with elbow monoarticular muscles. The preferred force directions of biarticular muscles were biased to the directions of elbow monoarticular muscles. Namely, the elbow joint is regulated by the simultaneous activation of monoarticular and biarticular muscles, whereas the shoulder joint is regulated dominantly by monoarticular muscles. These results suggest that biarticular muscles are innervated mainly to control the elbow joint during static force-regulation tasks. In addition, muscle regulation mechanisms for static force control tasks were found to be quite different from those during movements previously reported. The elbow single-joint stiffness was always higher than cross-joint stiffness (off-diagonal terms of the matrix) in static tasks while elbow single-joint stiffness is reported to be sometimes as small as cross-joint stiffness during movement. That is, during movements, the elbow monoarticular muscles were occasionally not activated when biarticular muscles were activated. In static tasks, however, monoarticular muscle components in single-joint stiffness were increased considerably whenever biarticular muscle components in single- and cross-joint stiffness increased. These observations suggest that biarticular muscles are not simply coupled with the innervation of elbow monoarticular muscles but also are regulated independently according to the required task. During static force-regulation tasks, covariation between biarticular and elbow monoarticular muscles may be required to increase stability and/or controllability or to distribute effort among the appropriate muscles.

scholarly journals Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

2011 ◽  
Vol 105 (4) ◽  
pp. 1633-1641 ◽  
Author(s):  
Xiao Hu ◽  
Wendy M. Murray ◽  
Eric J. Perreault
Keyword(s):  
Experimental Data ◽  
Short Range ◽  
Muscle Activation ◽  
Three Dimensional ◽  
Musculoskeletal Model ◽  
Muscle Stiffness ◽  
Muscle Properties ◽  
Human Arm ◽  
Wide Range ◽  
Endpoint Stiffness

The mechanical properties of the human arm are regulated to maintain stability across many tasks. The static mechanics of the arm can be characterized by estimates of endpoint stiffness, considered especially relevant for the maintenance of posture. At a fixed posture, endpoint stiffness can be regulated by changes in muscle activation, but which activation-dependent muscle properties contribute to this global measure of limb mechanics remains unclear. We evaluated the role of muscle properties in the regulation of endpoint stiffness by incorporating scalable models of muscle stiffness into a three-dimensional musculoskeletal model of the human arm. Two classes of muscle models were tested: one characterizing short-range stiffness and two estimating stiffness from the slope of the force-length curve. All models were compared with previously collected experimental data describing how endpoint stiffness varies with changes in voluntary force. Importantly, muscle properties were not fit to the experimental data but scaled only by the geometry of individual muscles in the model. We found that force-dependent variations in endpoint stiffness were accurately described by the short-range stiffness of active arm muscles. Over the wide range of evaluated arm postures and voluntary forces, the musculoskeletal model incorporating short-range stiffness accounted for 98 ± 2, 91 ± 4, and 82 ± 12% of the variance in stiffness orientation, shape, and area, respectively, across all simulated subjects. In contrast, estimates based on muscle force-length curves were less accurate in all measures, especially stiffness area. These results suggest that muscle short-range stiffness is a major contributor to endpoint stiffness of the human arm. Furthermore, the developed model provides an important tool for assessing how the nervous system may regulate endpoint stiffness via changes in muscle activation.

scholarly journals Verification of Finger Joint Stiffness Estimation Method With Soft Robotic Actuator

2020 ◽  
Vol 8 ◽  
Author(s):  
Xiang Qian Shi ◽  
Ho Lam Heung ◽  
Zhi Qiang Tang ◽  
Kai Yu Tong ◽  
Zheng Li
Keyword(s):  
Estimation Method ◽  
Joint Stiffness ◽  
Ground Truth ◽  
Clinical Score ◽  
Metacarpophalangeal Joint ◽  
Finger Joint ◽  
Joint Torque ◽  
Hand Rehabilitation ◽  
Stiffness Characteristic ◽  
Modified Ashworth Scale

Stroke has been the leading cause of disability due to the induced spasticity in the upper extremity. The constant flexion of spastic fingers following stroke has not been well described. Accurate measurements for joint stiffness help clinicians have a better access to the level of impairment after stroke. Previously, we conducted a method for quantifying the passive finger joint stiffness based on the pressure-angle relationship between the spastic fingers and the soft-elastic composite actuator (SECA). However, it lacks a ground-truth to demonstrate the compatibility between the SECA-facilitated stiffness estimation and standard joint stiffness quantification procedure. In this study, we compare the passive metacarpophalangeal (MCP) joint stiffness measured using the SECA with the results from our designed standalone mechatronics device, which measures the passive metacarpophalangeal joint torque and angle during passive finger rotation. Results obtained from the fitting model that concludes the stiffness characteristic are further compared with the results obtained from SECA-Finger model, as well as the clinical score of Modified Ashworth Scale (MAS) for grading spasticity. These findings suggest the possibility of passive MCP joint stiffness quantification using the soft robotic actuator during the performance of different tasks in hand rehabilitation.

Design Human-Robot Collaborative Lifting Task Using Optimization

2021 ◽  
Author(s):  
Asif Arefeen ◽  
Yujiang Xiang
Keyword(s):  
Degrees Of Freedom ◽  
Joint Angle ◽  
Joint Torque ◽  
Robotic Arm ◽  
Inverse Dynamic ◽  
Floating Base ◽  
Grasping Forces ◽  
Design Variables ◽  
Human Arm ◽  
Lifting Task

Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.

Minimum muscle tension-change model for human arm movement

1990 ◽  
Vol 11 ◽  
pp. S54 ◽  
Author(s):  
Yoji Uno ◽  
Mitsuo Kawato ◽  
Ryoji Suzuki
Keyword(s):  
Muscle Tension ◽  
Arm Movement ◽  
Change Model ◽  
Human Arm ◽  
Tension Change

Efficient Actor-Critic Reinforcement Learning With Embodiment of Muscle Tone for Posture Stabilization of the Human Arm

2021 ◽  
Vol 33 (1) ◽  
pp. 129-156
Author(s):  
Masami Iwamoto ◽  
Daichi Kato
Keyword(s):  
Reinforcement Learning ◽  
Muscle Tone ◽  
Muscle Tension ◽  
Experimental Studies ◽  
Control Policy ◽  
Humanoid Robots ◽  
Pedunculopontine Tegmental Nucleus ◽  
Learning Efficiency ◽  
Human Arm ◽  
Posture Stabilization

This letter proposes a new idea to improve learning efficiency in reinforcement learning (RL) with the actor-critic method used as a muscle controller for posture stabilization of the human arm. Actor-critic RL (ACRL) is used for simulations to realize posture controls in humans or robots using muscle tension control. However, it requires very high computational costs to acquire a better muscle control policy for desirable postures. For efficient ACRL, we focused on embodiment that is supposed to potentially achieve efficient controls in research fields of artificial intelligence or robotics. According to the neurophysiology of motion control obtained from experimental studies using animals or humans, the pedunculopontine tegmental nucleus (PPTn) induces muscle tone suppression, and the midbrain locomotor region (MLR) induces muscle tone promotion. PPTn and MLR modulate the activation levels of mutually antagonizing muscles such as flexors and extensors in a process through which control signals are translated from the substantia nigra reticulata to the brain stem. Therefore, we hypothesized that the PPTn and MLR could control muscle tone, that is, the maximum values of activation levels of mutually antagonizing muscles using different sigmoidal functions for each muscle; then we introduced antagonism function models (AFMs) of PPTn and MLR for individual muscles, incorporating the hypothesis into the process to determine the activation level of each muscle based on the output of the actor in ACRL. ACRL with AFMs representing the embodiment of muscle tone successfully achieved posture stabilization in five joint motions of the right arm of a human adult male under gravity in predetermined target angles at an earlier period of learning than the learning methods without AFMs. The results obtained from this study suggest that the introduction of embodiment of muscle tone can enhance learning efficiency in posture stabilization disorders of humans or humanoid robots.

Nonlinear stretch reflex interaction during cocontraction

1993 ◽  
Vol 69 (3) ◽  
pp. 943-952 ◽  
Author(s):  
R. R. Carter ◽  
P. E. Crago ◽  
P. H. Gorman
Keyword(s):  
Stretch Reflex ◽  
Joint Stiffness ◽  
Interphalangeal Joint ◽  
Joint Torque ◽  
Single Muscle ◽  
Extensor Pollicis Longus ◽  
Reflex Action ◽  
Antagonist Muscles ◽  
The Face ◽  
The Individual

1. We investigated the role of stretch reflexes in controlling two antagonist muscles acting at the interphalangeal joint in the normal human thumb. Reflex action was compared when either muscle contracted alone and during cocontraction. 2. The total torque of the flexor pollicis longus (FPL) and extensor pollicis longus (EPL) muscles was measured in response to an externally imposed extension of the interphalangeal joint. The initial joint angle and the amplitude of the extension were constant in all experiments, and the preload of the active muscle(s) was varied. Joint torque was measured at the peak of short-latency stretch reflex action during contraction of the FPL alone, contraction of the EPL alone, and during cocontraction. Incremental joint stiffness was calculated as the change in torque divided by the change in angle. 3. Incremental stiffness increased in proportion to the preload torque during single muscle contractions of either the FPL (lengthening disturbances) or the EPL (shortening disturbances). Thus stiffness was not regulated to a constant value in the face of varying loads for either single muscle stretch or release. 4. Incremental stiffness varied across the range of cocontraction levels while the net torque was maintained at approximately 0. Thus net torque alone did not determine the stiffness during cocontraction. 5. The contributions of each muscle to the net intrinsic torque during cocontraction were estimated by scaling the individual muscles' responses so that their sum gave the best fit (in a least-squares sense) to the cocontraction torque before reflex action. The solution is unique because the individual torques have opposite signs, but the stiffnesses add. This gave estimates of the initial torques of both muscles during cocontraction. 6. The contributions of the two muscles during cocontraction were used to estimate the active joint stiffness that would be expected if the two muscles were activated independently to the same levels as in the cocontraction trials. The stiffness measured at the peak of stretch reflex action during cocontraction trials differed from the sum of the stiffnesses of the two muscles when they were contracting alone. At low cocontraction levels, the measured stiffness was less than expected on the basis of summation of the action of the two muscles, whereas at high cocontraction levels, the measured stiffness was greater than expected. This demonstrates that there is nonlinear stretch reflex interaction. That is, reflex action for a pair of antagonists is not simply the linear sum of the reflex actions of the two muscles acting independently.(ABSTRACT TRUNCATED AT 400 WORDS)

NON-IONIZING (ULTRA-WIDEBAND FREQUENCY) ELECTROMAGNETIC RADIATION EFFECT ON NERVE FIBER ACTION POTENTIAL OF HUMAN BODY

2016 ◽  
Vol 78 (5-6) ◽  
Author(s):  
Muhammad Syafiq Noor Azizi ◽  
Azahari Salleh ◽  
Adib Othman ◽  
Nor Azlan Mohd Aris ◽  
Najmiah Radiah Mohamad
Keyword(s):  
Electromagnetic Radiation ◽  
Nerve Fiber ◽  
Human Body ◽  
Radiation Effect ◽  
The Body ◽  
Ultra Wideband ◽  
Physiological Data ◽  
Radio System ◽  
Human Arm ◽  
The Way

In modern telemedicine systems the physiological data of patients can be measured with the aid of electronic sensors located on and inside the human body. The collected medical data is then transmitted wirelessly to an external unit for processing, thereby enhancing the health monitoring, diagnosis, and therapy of the patients. In biomedical application, the process requires transmitting data, images and videos from inside the body taken by a radio system of a size of a pill seems to be the way. The use of non-ionizing electromagnetic radiation in various areas like medical application has arisen the electromagnetic radiation problem. The services provided by this type of application can cause either good or bad effects on human body depending on the power level, frequency and the way it being used. The implant antenna with ultra-wideband (UWB) frequency will be used by inserting it into the nerve of human arm in term of homogenous model. Ultra-wideband (UWB) is a wireless technology that potential applications in variety of medical areas such as implant wireless sensors, microwave hyperthermia, imaging and radar. It can transmit digital data over a wide frequency spectrum with very low power and at very high data rates. Hence, this paper present the non-ionizing electromagnetic radiation effect on electrical nerve fiber of human arm model with the presence of other human tissues such as fat, muscle, skin and etc. at ultra-wideband frequency which is expected to improve the understanding of radio propagation inside human body hence contribute to more advance and innovative medical implants. CST Microwave Studio is one of the EM modeling code which can be used for bio electromagnetic purpose.

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