Planar Arm Movements Research Articles

Comparison between sEMG and force as control interfaces to support planar arm movements in adults with Duchenne: a feasibility study

BackgroundAdults with Duchenne muscular dystrophy (DMD) can benefit from devices that actively support their arm function. A critical component of such devices is the control interface as it is responsible for the human-machine interaction. Our previous work indicated that surface electromyography (sEMG) and force-based control with active gravity and joint-stiffness compensation were feasible solutions for the support of elbow movements (one degree of freedom). In this paper, we extend the evaluation of sEMG- and force-based control interfaces to simultaneous and proportional control of planar arm movements (two degrees of freedom).MethodsThree men with DMD (18–23 years-old) with different levels of arm function (i.e. Brooke scores of 4, 5 and 6) performed a series of line-tracing tasks over a tabletop surface using an experimental active arm support. The arm movements were controlled using three control methods: sEMG-based control, force-based control with stiffness compensation (FSC), and force-based control with no compensation (FNC). The movement performance was evaluated in terms of percentage of task completion, tracing error, smoothness and speed.ResultsFor subject S1 (Brooke 4) FNC was the preferred method and performed better than FSC and sEMG. FNC was not usable for subject S2 (Brooke 5) and S3 (Brooke 6). Subject S2 presented significantly lower movement speed with sEMG than with FSC, yet he preferred sEMG since FSC was perceived to be too fatiguing. Subject S3 could not successfully use neither of the two force-based control methods, while with sEMG he could reach almost his entire workspace.ConclusionsMovement performance and subjective preference of the three control methods differed with the level of arm function of the participants. Our results indicate that all three control methods have to be considered in real applications, as they present complementary advantages and disadvantages. The fact that the two weaker subjects (S2 and S3) experienced the force-based control interfaces as fatiguing suggests that sEMG-based control interfaces could be a better solution for adults with DMD. Yet force-based control interfaces can be a better alternative for those cases in which voluntary forces are higher than the stiffness forces of the arms.

Human-Like Rewards to Train a Reinforcement Learning Controller for Planar Arm Movement

High-level spinal cord injury (SCI) in humans causes paralysis below the neck. Functional electrical stimulation (FES) technology applies electrical current to nerves and muscles to restore movement, and controllers for upper extremity FES neuroprostheses calculate stimulation patterns to produce desired arm movement. However, currently available FES controllers have yet to restore natural movements. Reinforcement learning (RL) is a reward-driven control technique; it can employ user-generated rewards, and human preferences can be used in training. To test this concept with FES, we conducted simulation experiments using computer-generated “pseudo-human” rewards. Rewards with varying properties were used with an actor-critic RL controller for a planar two-degree-of-freedom biomechanical human arm model performing reaching movements. Results demonstrate that sparse, delayed pseudo-human rewards permit stable and effective RL controller learning. The frequency of reward is proportional to learning success, and human-scale sparse rewards permit greater learning than exclusively automated rewards. Diversity of training task sets did not affect learning. Long-term stability of trained controllers was observed. Using human-generated rewards to train RL controllers for upper-extremity FES systems may be useful. Our findings represent progress toward achieving human–machine teaming in control of upper-extremity FES systems for more natural arm movements based on human user preferences and RL algorithm learning capabilities.

Enhancements to the Planar Active Handrest

The Planar Active Handrest (PAHR) is a large workspace assistive device that improves user precision manipulation. The PAHR is restricted in its use, because it does not rotate in the horizontal plane, which limits the workspace size and reduces user comfort. This paper evaluates an improved device, the Enhanced Planar Active Handrest (E-PAHR), which allows lateral/medial rotation of the upper arm. Under the E-PAHR design, the desired hand position can be obtained with redundant arm and device configurations. As such, we gave consideration to controller input choices and resultant device motions that are available. Three experiments evaluated the controller designs to select the most effective method to control our device. We conclude that a rotational DOF (degrees of freedom) allows the E-PAHR to better follow the kinematics of a user's planar arm movements while allowing skill level equal to the PAHR, with reduced user force input and lower perceived exertion.

3DOM: a 3 degree of freedom manipulandum to investigate redundant motor control.

This paper presents a novel robotic interface to investigate the neuromechanical control of redundant planar arm movements. A unique aspect of this device is the third axis by which the wrist, and hence the pose of the arm can be fully constrained. The topology is based on a 5R, closed loop pantograph, with a decoupled wrist flexion/extension cable actuated mechanism. The design and characterization (in terms of range of motion, impedance, friction and dynamics) are described in this paper. This device is lightweight, safe and has high force capabilities and low impedance. Simple experiments illustrate the advantages of this device for the investigation of redundant motor control in humans.

Planar arm movement trajectory formation: An optimization based simulation study

Rehabilitation of post stroke patients with upper extremity motor deficits is typically focused on relearning of motor abilities and functionalities requiring interaction with physiotherapists and/or rehabilitation robots. In a point-to-point movement training, the trajectories are usually arbitrarily determined without considering the motor impairment of the individual. In this paper, we used an optimal control model based on arm dynamics enabling also incorporation of muscle functioning constraints (i.e. simulation of muscle tightness) to find the optimal trajectories for planar arm reaching movements. First, we tested ability of the minimum joint torque cost function to replicate the trajectories obtained in previously published experimental trials done by neurologically intact subjects, and second, we predicted the optimal trajectories when muscle constraints were modeled. The resulting optimal trajectories show considerable similarity as compared to the experimental data, while on the other hand, the muscle constraints play a major role in determination of the optimal trajectories for stroke rehabilitation.

Comparative Analysis of Methods for Estimating Arm Segment Parameters and Joint Torques From Inverse Dynamics

A common problem in the analyses of upper limb unfettered reaching movements is the estimation of joint torques using inverse dynamics. The inaccuracy in the estimation of joint torques can be caused by the inaccuracy in the acquisition of kinematic variables, body segment parameters (BSPs), and approximation in the biomechanical models. The effect of uncertainty in the estimation of body segment parameters can be especially important in the analysis of movements with high acceleration. A sensitivity analysis was performed to assess the relevance of different sources of inaccuracy in inverse dynamics analysis of a planar arm movement. Eight regression models and one water immersion method for the estimation of BSPs were used to quantify the influence of inertial models on the calculation of joint torques during numerical analysis of unfettered forward arm reaching movements. Thirteen subjects performed 72 forward planar reaches between two targets located on the horizontal plane and aligned with the median plane. Using a planar, double link model for the arm with a floating shoulder, we calculated the normalized joint torque peak and a normalized root mean square (rms) of torque at the shoulder and elbow joints. Statistical analyses quantified the influence of different BSP models on the kinetic variable variance for given uncertainty on the estimation of joint kinematics and biomechanical modeling errors. Our analysis revealed that the choice of BSP estimation method had a particular influence on the normalized rms of joint torques. Moreover, the normalization of kinetic variables to BSPs for a comparison among subjects showed that the interaction between the BSP estimation method and the subject specific somatotype and movement kinematics was a significant source of variance in the kinetic variables. The normalized joint torque peak and the normalized root mean square of joint torque represented valuable parameters to compare the effect of BSP estimation methods on the variance in the population of kinetic variables calculated across a group of subjects with different body types. We found that the variance of the arm segment parameter estimation had more influence on the calculated joint torques than the variance of the kinematics variables. This is due to the low moments of inertia of the upper limb, especially when compared with the leg. Therefore, the results of the inverse dynamics of arm movements are influenced by the choice of BSP estimation method to a greater extent than the results of gait analysis.

Effects of Wrist Tendon Vibration on Targeted Upper-Arm Movements in Poststroke Hemiparesis

Background. Impaired motor control of the upper extremity after stroke may be related to lost sensory, motor, and integrative functions of the brain. Artificial activation of sensory afferents might improve control of movement by adding excitatory drive to sensorimotor control structures. The authors evaluated the effect of wrist tendon vibration (TV) on paretic upper-arm stability during point-to-point planar movements. Methods. TV (70 Hz) was applied to the forearm wrist musculature of 10 hemiparetic stroke patients as they made center-out planar arm movements. End-point stability, muscle activity, and grip pressure were compared as patients stabilized at the target position for trials completed before, during, and after the application of the vibratory stimulus. Results. Prior to vibration, hand position fluctuated as participants attempted to maintain the hand at the target after movement termination. TV improved arm stability, as evidenced by decreased magnitude of hand tangential velocity at the target. Improved stability was accompanied by a decrease in muscle activity throughout the arm as well as a mean decrease in grip pressure. Conclusions. These results suggest that vibratory stimulation of the distal wrist musculature enhances stability of the proximal arm and can be studied further as a mode for improving end-point stability during reaching in hemiparetic patients.

Mechanisms for visuomotor adaptation to left–right reversed vision

Behavioral studies suggest that the adaptation of planar arm movements to rotated visual feedback is achieved by the interplay of a gradual process which slowly rotates participants’ responses by up to +/−90°, and a discrete process which changes the responses by means of axis inversion. The processes for adaptation to left–right reversed visual feedback are far less well understood. To clarify this issue, 12 healthy participants performed pointing movements to targets presented in eight different directions, before and during exposure to left–right reversed visual feedback. We quantified the direction of each response 150 ms after movement onset and analyzed the time-course of those directions throughout the adaptation phase, separately for different targets. For targets along the axis of inversion, we only found an increase of response variability, for targets perpendicular to that axis, we observed a discrete 180° change of response direction, and for diagonal targets, we found a discrete 180° change followed by a gradual “backward” shift of 90°. The present findings confirm that sensorimotor adaptation is based on discrete and gradual processes, that both types of processes can occur concurrently, and suggests that those processes can contribute to adaptation in a target-specific fashion.

Optimization and evaluation of a proportional derivative controller for planar arm movement

In most clinical applications of functional electrical stimulation (FES), the timing and amplitude of electrical stimuli have been controlled by open-loop pattern generators. The control of upper extremity reaching movements, however, will require feedback control to achieve the required precision. Here we present three controllers using proportional derivative (PD) feedback to stimulate six arm muscles, using two joint angle sensors. Controllers were first optimized and then evaluated on a computational arm model that includes musculoskeletal dynamics. Feedback gains were optimized by minimizing a weighted sum of position errors and muscle forces. Generalizability of the controllers was evaluated by performing movements for which the controller was not optimized, and robustness was tested via model simulations with randomly weakened muscles. Robustness was further evaluated by adding joint friction and doubling the arm mass. After optimization with a properly weighted cost function, all PD controllers performed fast, accurate, and robust reaching movements in simulation. Oscillatory behavior was seen after improper tuning. Performance improved slightly as the complexity of the feedback gain matrix increased.

Estimation of upper limb muscle forces by a biomechanical simulation model

The purpose of this study was to quantify individual upper limb muscle forces during planar arm movements by a biomechanical simulation model. This study includes two steps. First, a multi-linkage rigid body biomechanical simulation model was developed. The simulation model was developed to create equations of motion for the body segments and simulate the directional dependent characteristic of joint muscle forces. Second, human subjects were recruited to perform planar arm reaching task in different directions. Kinematic data collected by a motion capture system was used as the input to the simulation model. The simulation results were further interpreted and compared with previous findings. Potential application of this model to biomedical robotics design was also discussed.

A neural tracking and motor control approach to improve rehabilitation of upper limb movements

BackgroundRestoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an ad hoc markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.MethodsThe markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.ResultsThe proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.ConclusionA novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.

Separate Adaptive Mechanisms for Controlling Trajectory and Final Position in Reaching

We examined control of the hand's trajectory (direction and shape) and final equilibrium position in horizontal planar arm movements by quantifying transfer of learned visuomotor rotations between two tasks that required aiming the hand to the same spatial targets. In a trajectory-reversal task ("slicing"), the hand reversed direction within the target and returned to the origin. In a positioning task ("reaching"), subjects moved the hand to the target and held it there; cursor feedback was provided only after movement ended to isolate learning of final position from trajectory direction. We asked whether learning acquired in one task would transfer to the other. Transfer would suggest that the hand's entire trajectory, including its endpoint, was controlled using a common spatial plan. Instead we found minimal transfer, suggesting that the brain used different representations of target position to specify the hand's initial trajectory and its final stabilized position. We also observed asymmetrical practice effects on hand trajectory, including systematic curvature of reaches made after rotation training and hypermetria of untrained slice reversals after reach training. These are difficult to explain with a unified control model, but were replicated in computer simulations that specified the hand's initial trajectory and its final equilibrium position. Our results suggest that the brain uses different mechanisms to plan the hand's initial trajectory and final position in point-to-point movements, that it implements these control actions sequentially, and that trajectory planning does not account for specific impedance values to be implemented about the final stabilized posture.

A biologically inspired neural network controller for ballistic arm movements.

BackgroundIn humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented.MethodsThe developed system is composed of three main computational blocks: 1) a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2) a pulse generator, which is responsible for the creation of muscular synergies; and 3) a limb model based on two joints (two degrees of freedom) and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans.ResultsThe model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians.Curvature values are similar to those encountered in experimental measures with humans.The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector.ConclusionThe proposed system has been shown to properly simulate the development of internal models and to control the generation and execution of ballistic planar arm movements. Since the neural controller learns to manage movements on the basis of kinematic information and arm characteristics, it could in perspective command a neuroprosthesis instead of a biomechanical model of a human upper limb, and it could thus give rise to novel rehabilitation techniques.

A model of cerebellum stabilized and scheduled hybrid long-loop control of upright balance.

A recurrent integrator proportional integral derivative (PID) model that has been used to account for cerebrocerebellar stabilization and scaling of transcortical proprioceptive feedback in the control of horizontal planar arm movements has been augmented with long-loop force feedback and gainscheduling to describe the control of human upright balance. The cerebellar component of the controller is represented by two sets of gains that each provide linear scaling of same-joint and interjoint long-loop stretch responses between ankle, knee, and hip. The cerebral component of the model includes a single set of same-joint linear force feedback gains. Responses to platform translations of a three-segment body model operating under this hybrid proprioception and force-based long-loop control were simulated. With low-velocity platform disturbances, "ankle-strategy"-type postural recovery kinematics and electromyogram (EMG) patterns were generated using the first set of cerebeller control gains. With faster disturbances, balance was maintained by including the second set of gains cerebellar control gains that yielded "mixed ankle-hip strategy"-type kinematics and EMG patterns. The addition of small amounts of simulated muscular coactivation improved the fit to certain human datasets. It is proposed that the cerebellum switches control gainsets as a function of sensed body kinematic state. Reduction of cerebellar gains with a compensatory increase in muscular stiffness yielded posture recovery with abnormal motions consistent with those found in cerebellar disease. The model demonstrates that stabilized hybrid long-loop feedback with scheduling of linear gains may afford realistic balance control in the absence of explicit internal dynamics models and suggests that the cerebellum and cerebral cortex may contribute to balance control by such a mechanism.

Systematic errors of planar arm movements provide evidence for space categorization effects and interaction of multiple frames of reference.

Healthy humans performed arm movements in a horizontal plane, from an initial position toward remembered targets, while the movement and the targets were projected on a vertical computer monitor. We analyzed the mean error of movement endpoints and we observed two distinct systematic error patterns. The first pattern resulted in the clustering of movement endpoints toward the diagonals of the four quadrants of an imaginary circular area encompassing all target locations (oblique effect). The second pattern resulted in a tendency of movement endpoints to be closer to the body or equivalently lower than the actual target positions on the computer monitor (y-effect). Both these patterns of systematic error increased in magnitude when a time delay was imposed between target presentation and initiation of movement. In addition, the presence of a stable visual cue in the vicinity of some targets imposed a novel pattern of systematic errors, including minimal errors near the cue and a tendency for other movement endpoints within the cue quadrant to err away from the cue location. A pattern of systematic errors similar to the oblique effect has already been reported in the literature and is attributed to the subject's conceptual categorization of space. Given the properties of the errors in the present work, we discuss the possibility that such conceptual effects could be reflected in a broad variety of visuomotor tasks. Our results also provide insight into the problem of reference frames used in the execution of these aiming movements. Thus, the oblique effect could reflect a hand-centered reference frame while the y-effect could reflect a body or eye-centered reference frame. The presence of the stable visual cue may impose an additional cue-centered (allocentric) reference frame.

A Simple Model for Fast Planar Arm Movements; Optimising Mechanical Activation and Moment-arms of Uniarticular and Biarticular Arm Muscles

In this study a simple model of a two segmented arm with only two muscles is used to study an underarm throw and an overarm stroke. There is a uniarticular shoulder muscle and an elbow muscle with facultatively an additional moment arm on the shoulder. The effect of changing the shoulder moment of the latter muscle, and of changing its mechanical delay is measured. The effect of tendons is assessed, and of throwing or striking using heavy weights. Some of the trials are analysed in detail to answer the question why some results are better than others. With uniarticular muscles we always found a proximal-distal sequence in muscle activity, but with a biarticular muscle sometimes this sequence can be absent. Tendons and the weight that is held do influence the optimal delay, but not the optimal moment are. The model predicts a different optimal technique for a stroke with a light or a heavy tool.

Moment arms and lengths of human upper limb muscles as functions of joint angles

Modeling of musculoskeletal structures requires accurate data on anatomical parameters such as muscle lengths (MLs), moment arms (MAs) and those describing the upper limb position. Using a geometrical model of planar arm movements with three degrees of freedom, we present, in an analytical form, the available information on the relationship between MAs and MLs and joint angles for thirteen human upper limb muscles. The degrees of freedom included are shoulder flexion/extension, elbow flexion/extension, and either wrist flexion/extension (the forearm in supination) or radial/ulnar deviation (the forearm in mid-pronation). Previously published MA/angle curves were approximated by polynomials. ML/angle curves were obtained by combining the constant values of MLs (defined by the distance between the origin and insertion points for a specific upper limb position) with a variable part obtained by multiplying the MA (joint radius) and the joint angle. The MAs of the prime wrist movers in radial/ulnar deviation were linear functions of the joint angle ( R 2 ≥ 0.9904), while quadratic polynomials accurately described their MAs during wrist flexion/extensions. The relationship between MAs and the elbow angle was described by 2nd, 3rd or 5th-order polynomials ( R 2 ≥ 0.9904), with a lesser quality of fit for the anconeus ( R 2 = 0.9349). In the full range of angular displacements, the length of wrist, elbow and shoulder muscles can change by 8.5, 55 and 200%, respectively.

Influence of joint interactional effects on the coordination of planar two-joint arm movements.

We have examined EMG-movement relations in two-joint planar arm movements to determine the influence of interactional torques on movement coordination. Explicitly defined combinations of elbow movements (ranging from 20 to 70 degrees) and wrist movements (ranging from 20 to 40 degrees) were performed during a visual, step-tracking task in which subjects were specifically required to attend to the initial and final angles at each joint. In all conditions the wrist and elbow rotated in the same direction, that is, flexion-flexion or extension-extension. Elbow movement kinematics were only slightly influenced by motion about the wrist. In contrast, the trajectory of the wrist movement was significantly influenced by uncompensated reaction torques resulting from movement about the elbow joint. At any given wrist amplitude, wrist movement duration increased and peak velocity decreased as elbow amplitude increased. In addition, as elbow amplitude increased, wrist movement onset was progressively delayed relative to this elbow movement. Surprisingly, the changes between joint movement onsets were not accompanied by corresponding changes between agonist EMG onsets at the elbow and wrist joints. The mean difference in onset times between elbow and wrist agonists (22-30 ms) remained unchanged across conditions. In addition, a basic pattern of muscle activation that scaled with movement amplitude was observed at each joint. Phasic agonist activity at the wrist and elbow joints remained remarkably similar across conditions and thus the changes in joint movement onset could not be attributed to changes in the motor commands.(ABSTRACT TRUNCATED AT 250 WORDS)

Reprogramming of muscle activation patterns at the wrist in compensation for elbow reaction torques during planar two-joint arm movements.

The relationship between wrist kinematics, dynamics and the pattern of muscle activation were examined during a two-joint planar movement in which the two joints moved in opposite directions, i.e. elbow flexion/wrist extension and elbow extension/wrist flexion. Elbow movements (ranging from 10 to 70 deg) and wrist movements (ranging from 10 to 50 deg) were performed during a visual, step-tracking task in which subjects were required to attend to the initial and final angles at each joint. As the elbow amplitude increased, wrist movement duration increased and the wrist movement trajectories became quite variable. Analysis of the torques acting at the wrist joint showed that elbow movements produced reaction torques acting in the same direction as the intended wrist movement. Distinct patterns of muscle activation were observed at the wrist joint that were dependent on the relative magnitude of the elbow reaction torque in relation to the net wrist torque. When the magnitude of the elbow reaction torque was quite small, the wrist agonist was activated first. As the magnitude of the elbow reaction torque increased, activity in the wrist agonist decreased significantly. In conditions where the elbow reaction torque was much larger than the net wrist torque, the wrist muscle torque reversed direction to oppose the intended movement. This reversal of wrist muscle torque was directly associated with a change in the pattern of muscle activation where the wrist antagonist was activated prior to the wrist agonist. Our findings indicate that motion of the elbow joint is an important consideration in planning wrist movement. Specifically, the selection of muscle activation patterns at the wrist is dependent on the relative magnitude and direction of the elbow reaction torque in relation to the direction of wrist motion.

Scaling of joint torque during planar arm movements.

The present study scrutinized the "Motor Program" concept for aimed arm movements. Human subjects pointed at visual targets in a horizontal plane, with movements of varying starting positions, amplitudes and directions. We recorded movement kinematics and subsequently calculated the shoulder and elbow joint torque profiles. Our results indicate that the shape of torque profiles is rather uniform across movements and joints. We defined the size of those profiles by six "landmark variables", which could be subsequently reduced to three factors using factor analysis: one factor represented torque magnitude and two represented different aspects of torque timing. Additional analyses indicated that total torque duration is an important controlled signal. Our findings conform with the view that movements are executed by playing back scaled versions of prototypical joint torque profiles.