Kinematic construction of the trajectory of sequential arm movements

Trajectory of the index finger during grasping

The trajectory of the index finger during grasping movements was compared to the trajectories predicted by three optimization-based models. The three models consisted of minimizing the integral of the weighted squared joint derivatives along the path (inertia-like cost), minimizing torque change, and minimizing angular jerk. Of the three models, it was observed that the path of the fingertip and the joint trajectories, were best described by the minimum angular jerk model. This model, which does not take into account the dynamics of the finger, performed equally well when the inertia of the finger was altered by adding a 20 g weight to the medial phalange. Thus, for the finger, it appears that trajectories are planned based primarily on kinematic considerations at a joint level.

Movement curvature planning through force field internal models

Human motion studies have focused primarily on modeling straight point-to-point reaching movements. However, many goal-directed reaching movements, such as movements directed towards oneself, are not straight but rather follow highly curved trajectories. These movements are particularly interesting to study since they are essential in our everyday life, appear early in development and are routinely used to assess movement deficits following brain lesions. We argue that curved and straight-line reaching movements are generated by a unique neural controller and that the observed curvature of the movement is the result of an active control strategy that follows the geometry of one's body, for instance to avoid trajectories that would hit the body or yield postures close to the joint limits. We present a mathematical model that accounts for such an active control strategy and show that the model reproduces with high accuracy the kinematic features of human data during unconstrained reaching movements directed toward the head. The model consists of a nonlinear dynamical system with a single stable attractor at the target. Embodiment-related task constraints are expressed as a force field that acts on the dynamical system. Finally, we discuss the biological plausibility and neural correlates of the model's parameters and suggest that embodiment should be considered as a main cause for movement trajectory curvature.

Asymmetric interjoint feedback contributes to postural control of redundant multi-link systems

Maintaining the postural configuration of a limb such as an arm or leg is a fundamental neural control task that involves the coordination of multiple linked body segments. Biological systems are known to use a complex network of inter- and intra-joint feedback mechanisms arising from muscles, spinal reflexes and higher neuronal structures to stabilize the limbs. While previous work has shown that a small amount of asymmetric heterogenic feedback contributes to the behavior of these systems, a satisfactory functional explanation for this non-conservative feedback structure has not been put forth. We hypothesized that an asymmetric multi-joint control strategy would confer both an energetic and stability advantage in maintaining endpoint position of a kinematically redundant system. We tested this hypothesis by using optimal control models incorporating symmetric versus asymmetric feedback with the goal of maintaining the endpoint location of a kinematically redundant, planar limb. Asymmetric feedback improved endpoint control performance of the limb by 16%, reduced energetic cost by 21% and increased interjoint coordination by 40% compared to the symmetric feedback system. The overall effect of the asymmetry was that proximal joint motion resulted in greater torque generation at distal joints than vice versa. The asymmetric organization is consistent with heterogenic stretch reflex gains measured experimentally. We conclude that asymmetric feedback has a functionally relevant role in coordinating redundant degrees of freedom to maintain the position of the hand or foot.

Balance control during an arm raising movement in bipedal stance: which biomechanical factor is controlled?

In order to obtain new insight into the control of balance during arm raising movements in bipedal stance, we performed a biomechanical analysis of kinematics and dynamical aspects of arm raising movements by combining experimental work, large-scale models of the body, and techniques simulating human behavior. A comparison between experimental and simulated joint kinematics showed that the minimum torque change model yielded realistic trajectories. We then performed an analysis based on computer simulations. Since keeping the center of pressure (CoP) and the projection of the center of mass (CoM) inside the support area is essential for equilibrium, we modeled an arm raising movement where displacement of one or the other variable is limited. For this optimization model, the effects of adding equilibrium constraints on movement trajectories were investigated. The results show that: (a) the choice of the regulated variable influences the strategy adopted by the system and (b) the system was not able to regulate the CoM for very fast movements without compromising its balance. Consequently, we suggest that the system is able to maintain balance while raising the arm by only controlling the CoP. This may be done mainly by using hip mechanisms and controlling net ankle torque.

Modeling Kinematics and Dynamics of Human Arm Movements

A central problem in motor control relates to the coordination of the arm's many degrees of freedom. This problem concerns the many arm postures (kinematics) that correspond to the same hand position in space and the movement trajectories between begin and end position (dynamics) that result in the same arm postures. The aim of this study was to compare the predictions for arm kinematics by various models on human motor control with experimental data and to study the relation between kinematics and dynamics. Goal-directed arm movements were measured in 3-D space toward far and near targets. The results demonstrate that arm postures for a particular target depend on previous arm postures, contradicting Donders's law. The minimum-work and minimum-torque-change models, on the other hand, predict a much larger effect of initial posture than observed. These data suggest that both kinematics and dynamics affect postures and that their relative contribution might depend on instruction and task complexity.

Goal-equivalent joint coordination in pointing: affect of vision and arm dominance

This study used the uncontrolled manifold (UCM) approach to study joint coordination underlying the control of task-related variables important for success at reaching and pointing to targets. More combinations of joint motions are available to the control system to achieve task success than are strictly necessary. How this abundance of motor solutions is managed by the nervous system and whether and how the availability of visual information affects the solution to joint coordination was investigated in this study. The variability of joint angle combinations was partitioned into 2 components with respect to control of either the hand's path or the path of the arm's center of mass (CM). The goal-equivalent variability (GEV) component represents trial-to-trial fluctuations of the joint configuration consistent with a stable value of the hand or CM path. The other component, non-goal-equivalent variability (NGEV), led to deviations away from the desired hand or CM path. We hypothesized a style of control in which the NGEV component is selectively restricted while allowing a range of goal-equivalent joint combinations to be used to achieve stability of the hand and CM paths. Twelve healthy right-handed subjects reached across their body to the center of a circular target with both the right and left arms and with their eyes open or closed on different trials. When repeating the task with the same arm under identical task conditions, subjects used a range of goal-equivalent joint configurations to control the entire trajectory of both the hand's and the arm's CM motion, as well as the terminal position of the pointer-tip. Overall joint configuration variability was consistently larger in the middle of the movement, near the time of peak velocity. The style of joint coordination was qualitatively similar regardless of the arm used to point or the visual condition. Quantitative differences in the structure of joint coordination were present for the non-dominant arm, however, when pointing in the absence of vision of the hand and target. The results of this study suggest that the nervous system uses a control strategy that provides for a range of goal-equivalent, rather than unique, joint combinations to stabilize the values of important task-related variables, while selectively restricting joint configurations that change these values. The possible advantage of this style of control is discussed. Absence of vision during reaching affected joint coordination only quantitatively and only for the less skilled left arm, suggesting that the role of visual information may be greater when organizing the motor components of this arm.

Internal models for motor control and trajectory planning

A number of internal model concepts are now widespread in neuroscience and cognitive science. These concepts are supported by behavioral, neurophysiological, and imaging data; furthermore, these models have had their structures and functions revealed by such data. In particular, a specific theory on inverse dynamics model learning is directly supported by unit recordings from cerebellar Purkinje cells. Multiple paired forward inverse models describing how diverse objects and environments can be controlled and learned separately have recently been proposed. The 'minimum variance model' is another major recent advance in the computational theory of motor control. This model integrates two furiously disputed approaches on trajectory planning, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.