Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Simulation Series |
| Herausgeber/-innen | Jerzy W. Rozenblit, Johannes Sametinger |
| Seiten | 1-12 |
| Seitenumfang | 12 |
| Auflage | 6 |
| ISBN (elektronisch) | 9781510838253 |
| Publikationsstatus | Veröffentlicht - Apr. 2017 |
| Veranstaltung | 4th Modeling and Simulation in Medicine Symposium, MSM 2017, Part of the 2017 Spring Simulation Multi-Conference, SpringSim 2017 - Virginia Beach, USA / Vereinigte Staaten Dauer: 23 Apr. 2017 → 26 Apr. 2017 |
Publikationsreihe
| Name | Simulation Series |
|---|---|
| Nummer | 6 |
| Band | 49 |
| ISSN (Print) | 0735-9276 |
Abstract
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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Simulation Series. Hrsg. / Jerzy W. Rozenblit; Johannes Sametinger. 6. Aufl. 2017. S. 1-12 (Simulation Series; Band 49, Nr. 6).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Identification of motion-based action potentials in neural bundles using a continuous symbiotic system
AU - Klinger, Volkhard
AU - Bohlmann, Sebastian
AU - Szczerbicka, Helena
N1 - Funding Information: All in-vivo experiments are performed in collaboration with the Clinic for Neurosurgery at the Hannover Medical School (MHH).
PY - 2017/4
Y1 - 2017/4
N2 - 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.
AB - 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.
KW - Agent-based evolutionary computation
KW - Symbiotic loop
KW - Symbiotic simulation
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=85020680728&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85020680728
T3 - Simulation Series
SP - 1
EP - 12
BT - Simulation Series
A2 - Rozenblit, Jerzy W.
A2 - Sametinger, Johannes
T2 - 4th Modeling and Simulation in Medicine Symposium, MSM 2017, Part of the 2017 Spring Simulation Multi-Conference, SpringSim 2017
Y2 - 23 April 2017 through 26 April 2017
ER -