Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand’s movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991).
Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006).
The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors.
Movement is represented by the polar coordinates φ,θ of the movement direction (heading direction) and the angular frequency ω of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention).
Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time.
In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia.
In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor.
The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.
Iossifidis, Ioannis und Ianki Rano (2013). „Modeling Human Arm Motion by Means of Attractor Dynamics Approach“. In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013).
Grimme, Britta u.a. (2012). „Naturalistic Arm Movements during Obstacle Avoi- dance in 3D and the Identification of Movement Primitives“. In: Experimental brain research 222.3, S. 185–200.
Iossifidis, Ioannis, Gregor Schöner u.a. (2006). „Dynamical Systems Approach for the Autonomous Avoidance of Obstacles and Joint-limits for an Redundant Ro- bot Arm“. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, S. 580–585. isbn: 1-4244-0258-1.
Georgopoulos, AP (1991). „Higher order motor control“. In: Annual review of neu- roscience 14, S. 361–377.