This paper presents studies of the coordination of volun-tary human arm movements. A mathematical model is for-mulated which is shown to predict both the qualitative fea-tures and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled mathematically by defining an objective function, a measure of performance for any possi-ble movement. The unique trajectory which yields the best performance is determined using dynamic optimization the-ory. In the work presented here, the objective function is the square of the magnitude of jerk (rate of change of accelera-tion) of the hand integrated over the entire movement. This is equivalent to assuming that a major goal of motor coordi-nation is the production of the smoothest possible movement

In this paper we present an approach to robot arm control based on exploiting the dynamical properties of a simple neural network oscillator circuit coupled to the joints of an arm. The entrainment and input/output properties of the oscillators are used to perform a variety of tasks with the same architecture, without any modeling of the arm or its environment. The approach is implemented on two real robot arms, and has been used to tune into the resonant frequency of pendulums, perform multi-joint coordinated motion by turning cranks, and exploit the dynamics of a `Slinky' toy to coordinate the motion of two arms. By exploiting the coupling between the physical arm and the neural oscillator, a range of complex behaviors can be achieved with a very simple system. Keywords: Oscillator, Neural control, Neural network, Robot Manipulator, Rhythmic movement. Neural Control of Rhythmic Arm Movements 2 1 Introduction This paper describes the properties of a set of simple neural network os...

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an inverse relation between curvature and speed. How are such complex movements learned through attentive imitation? Novel movements may be made as a series of distinct segments, but a practiced movement can be made smoothly, with a continuous, often bellshaped, velocity profile. How does learning of complex movements transform reactive imitation into predictive, automatic performance? A neural model is developed which suggests how parietal and motor cortical mechanisms, such as difference vector encoding, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. To initiate movement, visual attention shifts along the shape to be imitated an...

Inertial measurement components, which sense either acceleration or angular rate, are being embedded into common user interface devices more frequently as their cost continues to drop dramatically. These devices hold a number of advantages over other sensing technologies: they measure relevant parameters for human interfaces and can easily be embedded into wireless, mobile platforms. The work in this dissertation demonstrates that inertial measurement can be used to acquire rich data about human gestures, that we can derive efficient algorithms for using this data in gesture recognition, and that the concept of a parameterized atomic gesture recognition has merit. Further we show that a framework combining these three levels of description can be easily used by designers to create robust applications.

Minimum (or minimal) principles are mathematical laws that were first used in physics: Hamilton's principle and Fermat's principle of least time are two famous example. In the past decade, a number of motor control theories have been proposed that are formally of the same kind as the minimum principles of physics, and some of these have been quite successful at predicting motor performance in a variety of tasks. The present paper provides a comprehensive review of this work. Particular attention is given to the relation between minimum theories in motor control and those used in other disciplines. Other issues around which the review is organized include: (1) the relation between minimum principles and structural models of motor planning and motor control, (2) the empirically-driven development of minimum principles and the danger of circular theorizing, and (3) the design of critical tests for minimum theories. Some perspectives for future research are discussed in the concluding section of the paper.

Abstract. Invariant methods for formulating and analying the mechanics of the skeleto-muscular system with geometric algebra are further developed and applied to reaching kinematics. This work is set in the context of a neurogeometry research program to develop a coherent mathematical theory of neural sensory-motor control systems. 1.

The control of arm movement involves an intricate interplay between descending commands, musculoskeletal mechanics and spinal reflexes. Computational studies focusing on brain mechanisms for generating movement commands would profit from the availability of a neuromuscular model that captures key complexities of the biological system while preserving an abstract framework to facilitate simulation. One important component of the model is the fractional power damping (FPD) that is produced by the stretch reflex. In this chapter, we describe a model that unites FPD and other prominent reflex features with a Hill-based lumped parameter model of the elbow musculature.

Abstract. Simulation of natural human movement has proven to be a challenging problem, difficult to be solved by more or less traditional bioinspired strategies. In opposition to several existing solutions, mainly based upon deterministic algorithms, a data-driven approach is presented herewith, which is able to grasp not only the natural essence of human movements, but also their intrinsic variability, the latter being a necessary feature for many ergonomic applications. For these purposes a recurrent Artificial Neural Network with some novel features (recurrent RPROP, state neurons, weighted cost function) has been adopted and combined with an original pre-processing step on experimental data, resulting in a new hybrid approach for data aggregation. Encouraging results on human hand reaching movements are also presented. 1

this paper is to determine whether such aimed movements in an insect are compensated against altered loading

Although most brain-machine interface studies have focused on decoding kinematic parameters of motion such as hand position and velocity, it is known that motor cortical activity also correlates with kinetic signals, including active hand force and joint torque. Here, we attempted to reconstruct torque trajectories of the shoulder and elbow joints from the activity of simultaneously recorded units in primary motor cortex (MI) as monkeys (Macaca Mulatta) made reaching movements in the horizontal plane. Using a linear filter decoding approach that considers the history of neuronal activity up to one second in the past, we found reconstruction performance nearly equal to that of Cartesian hand position, despite the considerably greater bandwidth of the torque signals. The addition of delayed position and velocity feedback to the decoder generated consistently better torque reconstructions, suggesting that simple limb-state feedback may be useful to optimize brain-machine interface performance. 1