Three-dimensional model to predict muscle forces and their relation to motor variances in reaching arm movements

A three-dimensional (3-D) arm movement model is presented to simulate kinematic properties and muscle forces in reaching arm movements. Healthy subjects performed reaching movements repetitively either with or without a load in the hand. Joint coordinates were measured. Muscle moment arms, 3-D angular acceleration, and moment of inertias of arm segments were calculated to determine 3-D joint torques. Variances of hand position, arm configuration, and muscle activities were calculated. Ratios of movement variances observed in the two conditions (load versus without load) showed no differences for hand position and arm configuration variances. Virtual muscle force variances for all muscles except deltoid posterior and EMG variances for four muscles increased significantly by moving with the load. The greatly increased variances in muscle activity did not imply equally high increments in kinematic variances. We conclude that enhanced muscle cooperation through synergies helps to stabilize movement at the kinematic level when a load is added.     

Proximal versus distal control of two-joint planar reaching movements in the presence of neuromuscular noise

Determining how the human nervous system contends with neuro-motor noise is vital to understanding how humans achieve accurate goal-directed movements. Experimentally, people learning skilled tasks tend to reduce variability in distal joint movements more than in proximal joint movements. This suggests that they might be imposing greater control over distal joints than proximal joints. However, the reasons for this remain unclear, largely because it is not experimentally possible to directly manipulate either the noise or the control at each joint independently. Therefore, this study used a 2 degree-of-freedom torque driven arm model to determine how different combinations of noise and/or control independently applied at each joint affected the reaching accuracy and the total work required to make the movement. Signal-dependent noise was simultaneously and independently added to the shoulder and elbow torques to induce endpoint errors during planar reaching. Feedback control was then applied, independently and jointly, at each joint to reduce endpoint error due to the added neuromuscular noise. Movement direction and the inertia distribution along the arm were varied to quantify how these biomechanical variations affected the system performance. Endpoint error and total net work were computed as dependent measures. When each joint was independently subjected to noise in the absence of control, endpoint errors were more sensitive to distal (elbow) noise than to proximal (shoulder) noise for nearly all combinations of reaching direction and inertia ratio. The effects of distal noise on endpoint errors were more pronounced when inertia was distributed more toward the forearm. In contrast, the total net work decreased as mass was shifted to the upper arm for reaching movements in all directions. When noise was present at both joints and joint control was implemented, controlling the distal joint alone reduced endpoint errors more than controlling the proximal joint alone for nearly all combinations of reaching direction and inertia ratio. Applying control only at the distal joint was more effective at reducing endpoint errors when more of the mass was more proximally distributed. Likewise, controlling the distal joint alone required less total net work than controlling the proximal joint alone for nearly all combinations of reaching distance and inertia ratio. It is more efficient to reduce endpoint error and energetic cost by selectively applying control to reduce variability in the distal joint than the proximal joint. The reasons for this arise from the biomechanical configuration of the arm itself.     

On the possibility of linear modelling the human arm neuromuscular apparatus

It has been widely claimed that linear models of the neuromuscular apparatus give very inaccurate approximations of human arm reaching movements. The present paper examines this claim by quantifying the contributions of the various non-linear effects of muscle force generation on the accuracy of linear approximation. We performed computer simulations of a model of a two-joint arm with six monarticular and biarticular muscles. The global actions of individual muscles resulted in a linear dependence of the joint torques on the joint angles and angular velocities, despite the great non-linearity of the muscle properties. The effect of time delay in force generation is much more important for model accuracy than all the non-linear effects, while ignoring this time delay in linear approximation results in large errors. Thus, the viscosity coefficients are rather underestimated and some of them can even be paradoxically estimated to be negative. Similarly, our computation showed that ignoring the time delay resulted in large errors in the estimation of the hand equilibrium trajectory. This could explain why experimentally estimated hand equilibrium trajectories may be complex, even during a simple reaching movement. The hand equilibrium trajectory estimated by a linear model becomes simple when the time delay is taken into account, and it is close to that actually used in the non-linear model. The results therefore provide a theoretical basis for estimating the hand equilibrium trajectory during arm reaching movements and hence for estimating the time course of the motor control signals associated with this trajectory, as set out in the equilibrium point hypothesis. Received: 17 February 1999 / Accepted in revised form: 22 October 1999     

Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model

In this study, human arm movement was re-constructed from electromyography (EMG) signals using a forward dynamics model acquired by an artificial neural network within a modular architecture. Dynamic joint torques at the elbow and shoulder were estimated for movements in the horizontal plane from the surface EMG signals of 10 flexor and extensor muscles. Using only the initial conditions of the arm and the EMG time course as input, the network reliably reconstructed a variety of movement trajectories. The results demonstrate that posture maintenance and multijoint movements, entailing complex via-point specification and co-contraction of muscles, can be accurately computed from multiple surface EMG signals. In addition to the model's empirical uses, such as calculation of arm stiffness during motion, it allows evaluation of hypothesized computational mechanisms of the central nervous system such as virtual trajectory control and optimal trajectory planning.     

Patterns of motor activity in the isolated nerve cord of the octopus arm

The extremely flexible octopus arm provides a unique opportunity for studying movement control in a highly redundant motor system. We describe a novel preparation that allows analysis of the peripheral nervous system of the octopus arm and its interaction with the muscular and mechanosensory elements of the arm's intrinsic muscular system. First we examined the synaptic responses in muscle fibers to identify the motor pathways from the axial nerve cord of the arm to the surrounding musculature. We show that the motor axons project to the muscles via nerve roots originating laterally from the arm nerve cord. The motor field of each nerve is limited to the region where the nerve enters the arm musculature. The same roots also carry afferent mechanosensory information from the intrinsic muscle to the axial nerve cord. Next, we characterized the pattern of activity generated in the dorsal roots by electrically stimulating the axial nerve cord. The evoked activity, although far reaching and long lasting, cannot alone account for the arm extension movements generated by similar electrical stimulation. The mismatch between patterns of activity in the isolated cord and in an intact arm may stem from the involvement of mechanosensory feedback in natural arm extension.     

Impedance characteristics of a neuromusculoskeletal model of the human arm I. Posture control

The mechanical impedance of neuromusculoskeletal models of the human arm is studied in this paper. The model analysis provides a better understanding of the contributions of possible intrinsic and reflexive components of arm impedance, makes clear the limitations of second-order mass-viscosity-stiffness models and reveals possible task effects on the impedance. The musculoskeletal model describes planar movements of the upper arm and forearm, which are moved by six lumped muscles with nonlinear dynamics. The motor control system is represented by a neural network which combines feedforward and feedback control. It is optimized for the control of movements or for posture control in the presence of external forces. The achieved impedance characteristics depend on the conditions during the learning process. In particular, the impedance is adapted in a suitable way to the frequency content and direction of external forces acting on the hand during an isometric task. The impedance characteristics of a model, which is optimized for movement control, are similar to experimental data in the literature. The achieved stiffness is, to a large extent, reflexively determined whereas the approximated viscosity is primarily due to intrinsic attributes. It is argued that usually applied Hill-type muscle models do not properly represent intrinsic muscle stiffness. Received: 14 October 1997 / Accepted in revised form: 18 May 1999     

The control of twisting somersaults

In the takeoff and early flight phase of a twisting somersault, joint coordination is based on feed-forward control whereas in the late stages of the flight phase configuration adjustments are made using feedback control to ensure accurate completion of the movement and appropriate landing orientation. The aim of this study was to use a computer simulation model of aerial movement to investigate the extent to which arm and hip movements can control twist and somersault rotation in the flight phase of a twisting somersault. Two mechanisms were considered for the control of twist in simulated target trampoline movements with flight times of 1.4 s. In the first case a single symmetrical arm adduction correction was made using delayed feedback control based on the difference between the twist rate in a perturbed simulation and the twist rate in a target movement comprising a forward somersault with 1½ twists. Final corrections were made using symmetrical arm abduction and hip flexion to adjust the twist and somersault angles. In the second case continual asymmetrical arm adduction/abduction adjustments were used to remove the tilt from a perturbed full twisting backward somersault using delayed feedback control based on twist angle and angular velocity. The first method was able to cope with perturbations to a forward somersault with 1½ twists providing the feedback time delay was less than 200 ms. The second method was able to correct a perturbed full twisting backward somersault providing the feedback time delay was less than 125 ms.     

Mutual influences of upper and lower extremities during cyclic movements

The possibility for the activation of muscles in a passive arm during its cyclic movements imposed by active movements of the contralateral arm or by an experimenter and the effect that the movements of lower extremities have on the activity of the arm muscles have been studied. In addition, the activity of the leg muscles was studied as dependent on the motor task performed by the arms. Ten healthy subjects performed antiphase arm movements with and without stepping-like movements of both legs in the supine position. The experiment was performed under three conditions for the arm movements: (1) both arms performed active movements; (2) one arm performed active movements, and the contralateral arm, being entirely passive, was forced to participate in movements; (3) the movement of the passive arm was caused by an experimenter. Under condition (2), additional loadings of 30 and 60 N were applied to the active arm. Under all conditions, the arm movements were performed with and without leg movements. The possibility for the activation of muscles in the arm performing passive movements has been demonstrated. To a large extent, this is possible due to an increase in the afferent inflow from the muscles of the contralateral arm. The electrical activity was modulated during cyclic arm movements and depended on the level of loading of the active arm. During the combined active movements of the arms and legs, the reduction in the activity of the flexor muscles of the shoulder and forearm was observed. In the case of passive stepping-like movements, the concomitant arm movements increased the magnitude of electromyographic bursts in most of the examined leg muscles. During active leg movements, a similar increase in electromyographic bursts was observed only in the m. biceps femoris (BF) and the anterior tibial muscle. An increase in the loading of one arm caused a significant increase in the EMG activity in most examined muscles of the legs. The data obtained provide additional proof for the existence of a functionally significant neuronal interaction between the arms, as well as between the upper and lower extremities, which is probably due to intraspinal neuronal connections.     

Analysis of an optimal control model of multi-joint arm movements

 In this paper, we propose a model of biological motor control for generation of goal-directed multi-joint arm movements, and study the formation of muscle control inputs and invariant kinematic features of movements. The model has a hierarchical structure that can determine the control inputs for a set of redundant muscles without any inverse computation. Calculation of motor commands is divided into two stages, each of which performs a transformation of motor commands from one coordinate system to another. At the first level, a central controller in the brain accepts instructions from higher centers, which represent the motor goal in the Cartesian space. The controller computes joint equilibrium trajectories and excitation signals according to a minimum effort criterion. At the second level, a neural network in the spinal cord translates the excitation signals and equilibrium trajectories into control commands to three pairs of antagonist muscles which are redundant for a two-joint arm. No inverse computation is required in the determination of individual muscle commands. The minimum effort controller can produce arm movements whose dynamic and kinematic features are similar to those of voluntary arm movements. For fast movements, the hand approaches a target position along a near-straight path with a smooth bell-shaped velocity. The equilibrium trajectories in X and Y show an ‘N’ shape, but the end-point equilibrium path zigzags around the hand path. Joint movements are not always smooth. Joint reversal is found in movements in some directions. The excitation signals have a triphasic (or biphasic) pulse pattern, which leads to stereotyped triphasic (or biphasic) bursts in muscle control inputs, and a dynamically modulated joint stiffness. There is a fixed sequence of muscle activation from proximal muscles to distal muscles. The order is preserved in all movements. For slow movements, it is shown that a constant joint stiffness is necessary to produce a smooth movement with a bell-shaped velocity. Scaled movements can be reproduced by varying the constraints on the maximal level of excitation signals according to the speed of movement. When the inertial parameters of the arm are altered, movement trajectories can be kept invariant by adjusting the pulse height values, showing the ability to adapt to load changes. These results agree with a wide range of experimental observations on human voluntary movements. Received: 4 December 1995 / Accepted in revised form: 17 September 1996     

A new approach to reaching control for tetraplegic subjects

A simple upper limb control strategy to guide reaching in preparation to grasp for tetraplegic subjects is proposed. The control is based on new studies of self-paced human arm movements involving rotations about the shoulder and elbow joints. An experimental study of reaching, while grasping, by able-bodied humans, allowed us to reduce the dimensionality of the control vector from two to a single variable. This was accomplished by detailed analysis of the synergy between shoulder and elbow joint angles. This study examined only movements in teh horizontal plane. In the experiments we varied: (a) the shape of targets; (b) their position relative to the initial position of the hand; and (c) the speed of reaching. A synergy between shoulder and elbow joint angles was found in most analysed movements, and it was characterized by a scaling parameter between elbow and shoulder angular velocities. The scaling parameter was determined from the target position presented in the visual perceptive field and initial shoulder and elbow angles. The same experimental setup in studies with tetraplegics with retained shoulder movements showed that this natural synergism is preserved even though the motor and sensory components of the upper limb are reduced or absent. Tetraplegics originally showed a very different reaching pattern, but after short training sessions they developed a reaching behaviour which was similar to able-bodied subjects. The results presented can be used in the following way: a tetraplegic subject lacking elbow extension and flexion may be fitted with an assistive system which will be volitionally controlled only from ipsilateral shoulder movements. The assistive system can comprise either a motorized brace, or a functional electrical stimulation system applied to elbow flexors and extensors. With this system volitional movements at the shoulder would bring the hand into the correct position to accomplish an assisted grasping motion.     

Quantization of human motions and learning of accurate movements

This paper presents a mathematical model for the learning of accurate human arm movements. Its main features are that the movement is the superposition of smooth submovements, the intrinsic deviation of arm movements is considered, visual and kinesthetic feedback are integrated in the motion control, and the movement duration and accuracy are optimized with practice. This model is consistent with the jerky arm movements of infants, and may explain how the adult motion behavior emerges from the infant behavior. Comparison with measurements of adult movements shows that the kinematics of accurate movements are well predicted by the model. Received: 15 May 1997 / Accepted 5 December 1997     

Interlimb interactions during cyclic in-phase and antiphase movements of arms and legs and their dependence on afferent influences

Coordinated arm and leg movements imply neural interactions between the rhythmic generators of the upper and lower extremities. In ten healthy subjects in the lying position, activity of the muscles of the upper and lower extremities was recorded during separate and joint cyclic movements of the arms and legs with different phase relationships between the movements of the limbs and under various conditions of the motor task. Antiphase active arm movements were characterized by higher muscle activity than during the inphase mode. The muscle activity during passive arm movements imposed by the experimentalist was significantly lower than muscle activity during passive arm movements imposed by the other arm. When loading one arm, the muscle activity in the other, passively moving, arm increased independently from the synergy of arm movements. During a motor task implementing joint antiphase movements of both upper and lower extremities, compared to a motor task implementing their joint in-phase movements, we observed a significant increase in activity in the biceps brahii muscle, the tibialis anterior muscle, and the biceps femoris muscle. Loading of arms in these motor tasks has been accompanied by increased activity in some leg muscles. An increase in the frequency of rhythmic movements resulted in a significant growth of the muscle activity of the arms and legs during their cooperative movements with a greater rate of rise in the flexor muscle activity of the arms and legs during joint antiphase movements. Thus, both the spatial organization of movements and the type of afferent influences are significant factors of interlimb interactions, which, in turn, determine the type of neural interconnections that are involved in movement regulation.     

Robot-based methodology for a kinematic and kinetic analysis of unconstrained,but reproducible upper extremity movement

Although arm movements play an important role in everyday life, there is still a lack of procedures for the analysis of upper extremity movement. The main problems for standardizing the procedure are the variety of arm movements and the difficult assessment of external hand forces. The first problem requires the predefinition of motions, and the second one is the prerequisite for calculation of net joint forces and torques arising during motion. A new methodology for measuring external forces during prespecified, reproducible upper extremity movement has been introduced and validated. A robot-arm has been used to define the motion and 6 degrees of freedom (DoF) force sensor has been attached to it for acquiring the external loads acting on the arm. Additionally, force feedback has been used to help keeping external loads constant. Intra-individual reproducibility of joint angles was estimated by using correlation coefficients to compare a goal-directed movement with robot-guided task. Inter-individual reproducibility has been evaluated by using the mean standard deviation of joint angles for both types of movement. The results showed that both inter- and intra-individual reproducibility have significantly improved by using the robot. Also, the effectiveness of using force feedback for keeping a constant external load has been shown. This makes it possible to estimate net joint forces and torques which are important biomechanical information in motion analysis.     

The inactivation principle: mathematical solutions minimizing the absolute work and biological implications for the planning of arm movements

An important question in the literature focusing on motor control is to determine which laws drive biological limb movements. This question has prompted numerous investigations analyzing arm movements in both humans and monkeys. Many theories assume that among all possible movements the one actually performed satisfies an optimality criterion. In the framework of optimal control theory, a first approach is to choose a cost function and test whether the proposed model fits with experimental data. A second approach (generally considered as the more difficult) is to infer the cost function from behavioral data. The cost proposed here includes a term called the absolute work of forces, reflecting the mechanical energy expenditure. Contrary to most investigations studying optimality principles of arm movements, this model has the particularity of using a cost function that is not smooth. First, a mathematical theory related to both direct and inverse optimal control approaches is presented. The first theoretical result is the Inactivation Principle, according to which minimizing a term similar to the absolute work implies simultaneous inactivation of agonistic and antagonistic muscles acting on a single joint, near the time of peak velocity. The second theoretical result is that, conversely, the presence of non-smoothness in the cost function is a necessary condition for the existence of such inactivation. Second, during an experimental study, participants were asked to perform fast vertical arm movements with one, two, and three degrees of freedom. Observed trajectories, velocity profiles, and final postures were accurately simulated by the model. In accordance, electromyographic signals showed brief simultaneous inactivation of opposing muscles during movements. Thus, assuming that human movements are optimal with respect to a certain integral cost, the minimization of an absolute-work-like cost is supported by experimental observations. Such types of optimality criteria may be applied to a large range of biological movements.     

Influence of Vibration on Motor Responses in the Upper Arm Muscles under Stationary Conditions and during Voluntary and Evoked Arm Movements under Limb Unloading Conditions

Locomotion of mammals, including humans, is based on the rhythmic activity of spinal cord circuitries. The functioning of these circuitries depends on multimodal afferent information and on supraspinal influences from the motor cortex. Using the method of transcranial magnetic stimulation (TMS) of arm muscle areas in the motor cortex, we studied the motor evoked potentials (MEP) in the upper arm muscles in stationary conditions and during voluntary and vibration-evoked arm movements. The study included 13 healthy subjects under arm and leg unloading conditions. In the first series of experiments, with motionless limbs, the effect of vibration of left upper arm muscles on motor responses in these muscles was evaluated. In the second series of experiments, MEP were compared in the same muscles during voluntary and rhythmic movements generated by left arm m. triceps brachii vibration (the right arm was stationary). Motionless left arm vibration led to an increase in MEP values in both vibrated muscle and in most of the non-vibrated muscles. For most target muscles, MEP was greater with voluntary arm movements than with vibration-evoked movements. At the same time, a similar MEP modulation in the cycle of arm movements was observed in the same upper arm muscles during both types of arm movements. TMS of the motor cortex significantly potentiated arm movements generated by vibration, but its effect on voluntary movements was weaker. These results indicate significant differences in the degree of motor cortex involvement in voluntary and evoked arm movements. We suppose that evoked arm movements are largely due to spinal rather than central mechanisms of generation of rhythmic movements.     

Mutual influences of upper and lower extrimities during cyclic movements

The possibility of muscle activation of passive arm during its cyclic movements, imposed by active movements of contralateral arm or by experimenter was studied, as well as the influence of lower extremities cyclic movements onto arm muscles activity. In addition to that the activity of legs muscles was estimated in dependence on motor task condition for arms. Ten healthy supine subjects carried out opposite movements of arms with and without stepping-like movements of both legs. The experiment included three conditions for arm movements: 1) the active movements of both arms; 2) the active movements of one arm, when other entirely passive arm participated in the movement by force; 3) passive arm movement caused by experimenter. In the condition 2) additional load on active arm was applied (30 N and 60 N). In all three conditions the experiment was carried out with arms movements only or together with legs movements. The capability of passive moving arm muscles activation depended on increasing afferent inflow from muscles of contralateral arm was demonstrated. Emerging electrical activity was modulated in the arms movements cycle and depended on the degree of active arm loading. During combined active movements of arms and legs the reduction of activity in the flexor muscles of shoulder and forearm was observed. Concomitant arms movements increased the magnitude ofelectromiographic bursts during passive stepping-like movements in the most of recorded muscles, and the same increasing was only observed in biceps femoris and tibialis anterior muscles during active legs movement. The increasing of loading of one arm caused essential augmentation of EMG-activity in the majority of recording legs muscles. The data obtained are the additional proof of existence of functionally significant neuronal interaction both between arms and between upper and lower extremities, which is evidently depend on the intraspinal neuronal connections.     

The Temporal Structure of Vertical Arm Movements

The present study investigates how the CNS deals with the omnipresent force of gravity during arm motor planning. Previous studies have reported direction-dependent kinematic differences in the vertical plane; notably, acceleration duration was greater during a downward than an upward arm movement. Although the analysis of acceleration and deceleration phases has permitted to explore the integration of gravity force, further investigation is necessary to conclude whether feedforward or feedback control processes are at the origin of this incorporation. We considered that a more detailed analysis of the temporal features of vertical arm movements could provide additional information about gravity force integration into the motor planning. Eight subjects performed single joint vertical arm movements (45° rotation around the shoulder joint) in two opposite directions (upwards and downwards) and at three different speeds (slow, natural and fast). We calculated different parameters of hand acceleration profiles: movement duration (MD), duration to peak acceleration (D PA), duration from peak acceleration to peak velocity (D PA-PV), duration from peak velocity to peak deceleration (D PV-PD), duration from peak deceleration to the movement end (D PD-End), acceleration duration (AD), deceleration duration (DD), peak acceleration (PA), peak velocity (PV), and peak deceleration (PD). While movement durations and amplitudes were similar for upward and downward movements, the temporal structure of acceleration profiles differed between the two directions. More specifically, subjects performed upward movements faster than downward movements; these direction-dependent asymmetries appeared early in the movement (i.e., before PA) and lasted until the moment of PD. Additionally, PA and PV were greater for upward than downward movements. Movement speed also changed the temporal structure of acceleration profiles. The effect of speed and direction on the form of acceleration profiles is consistent with the premise that the CNS optimises motor commands with respect to both gravitational and inertial constraints.     

Kinematic and EMG characteristics of simple shoulder movements with proprioception and visual feedback.

The objective of this study was to determine if simple, shoulder movements use the dual control hypothesis strategy, previously demonstrated with elbow movements, and to see if this strategy also applies in the absence of visual feedback. Twenty subjects were seated with their right arm abducted to 90 degrees and externally rotated in the scapular plane. Subjects internally rotated to a target position using a custom shoulder wheel at three different speeds with and without visual feedback. Kinematics were collected with a motion analysis system and electromyographic (EMG) recordings of the pectoralis major (PECT), infraspinatus (INFRA), anterior and posterior (ADELT, PDELT) deltoid muscles were used to evaluate muscle activity patterns during movements. Kinematics changed as movement speed increased with less accuracy (p<0.01). Greater EMG activity was observed in the PECT, PDELT, and INFRA with shorter durations for the ADELT, PDELT and INFRA. Movements with only kinesthetic feedback were less accurate (p<0.01) and performed faster (p<0.01) than movements with visual feedback. EMG activity suggests no major difference in CNS control strategies in movements with and without visual feedback. Greater resolution with visual feedback enables the implementation of a dual control strategy, allowing greater movement velocity while maintaining accuracy.     

Virtual trajectory and stiffness ellipse during multijoint arm movement predicted by neural inverse models

We predict the virtual trajectories and stiffness ellipses during multijoint arm movements by computer simulations. A two-link manipulator with four single-joint muscles and two double-joint muscles is used as a model of the human arm. Physical parameters of the model are derived from several experimental data. Among them, special emphasis is put on low values of the dynamic hand stiffness recently measured during single joint and multijoint movements. The feedback-error-learning scheme to acquire the inverse dynamics model and the inverse statics model is utilized for this prediction. The virtual trajectories are much more complex than the actual trajectories. This indicates that planning the virtual trajectory is as difficult as solving the inverse dynamics problem for medium and fast movements, and simply falsifies the advocated computational advantage of the virtual trajectory control hypothesis. Thus, we conclude that learning inverse models is essential even in the virtual trajectory control framework. Finally, we propose a new computational model to learn the complicated shape of the virtual trajectories by integrating the virtual trajectory control and the feedback-error-learning scheme.     

A behavior-based inverse kinematics algorithm to predict arm prehension postures for computer-aided ergonomic evaluation.

In this paper, the computational problem of inverse kinematics of arm prehension movements was investigated. How motions of each joint involved in arm movements can be used to control the end-effector (hand) position and orientation was first examined. It is shown that the inverse kinematics problem due to the kinematic redundancy in joint space is ill-posed only at the control of hand orientation but not at the control of hand position. Based upon this analysis, a previously proposed inverse kinematics algorithm (Wang et Verriest, 1998a) to predict arm reach postures was extended to a seven-DOF arm model to predict arm prehension postures using a separate control of hand position and orientation. The algorithm can be either in rule-based form or by optimization through appropriate choice of weight coefficients. Compared to the algebraic inverse kinematics algorithm, the proposed algorithm can handle the non-linearity of joint limits in a straightforward way. In addition, no matrix inverse calculation is needed, thus avoiding the stability and convergence problems often occurring near a singularity of the Jacobian. Since an end-effector motion-oriented method is used to describe joint movements, observed behaviors of arm movements can be easily implemented in the algorithm. The proposed algorithm provides a general frame for arm postural control and can be used as an efficient postural manipulation tool for computer-aided ergonomic evaluation.