Identification of motion-based action potentials in neural bundles using a continuous symbiotic system

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The identification of motion- and sensory feedback-based action potentials in peripheral nerves is a great challenge in medical technology. It is the prerequisite for applications like prosthesis control or limb stimulation. Based on the acquisition of action potentials, the identification process correlates physiological and motion-based parameters to match movement trajectories and the corresponding action potentials. In this paper we focus on the identification method based on a data driven approach and its verification. We present the closed-loop identification method, implemented using a symbiotic continuous system (Aydt, Turner, Cai, and Low 2008), (Aydt, Turner, Cai, and Low 2009), consisting of a robotic based trajectory generation, the nerve simulation and, an agent-based machine learning system. We introduce the model generation process, showing an emergent behavior and present results of different scenarios generated using synthetic data sets. We show the whole verification approach of the identification method and illustrate the influence of the identification parameters on the quality of results.