{"title":"SMoBAICS","authors":"V. Klinger","doi":"10.4018/978-1-7998-8050-9.ch015","DOIUrl":null,"url":null,"abstract":"Simulation and modelling are powerful methods in computer aided therapy, rehabilitation monitoring, identification and control. The smart modular biosignal acquisition and identification system (SMoBAICS) provides methods and techniques to acquire electromyogram (EMG)- and electroneurogram (ENG)-based data for the evaluation and identification of biosignals. In this paper the author focuses on the development, integration and verification of platform technologies which support this entire data processing. Simulation and verification approaches are integrated to evaluate causal relationships between physiological and bioinformatical processes. Based on this we are stepping up of efforts to develop substitute methods and computer-aided simulation models with the objective of reducing animal testing. This work continues the former work about system identification and biosignal acquisition and verification systems presented in (Bohlmann et al., 2010), (Klinger and Klauke, 2013), (Klinger, 2014). This paper focuses on the next generation of an embedded data acquisition and identification system and its flexible platform architecture. Different application scenarios are shown to illustrate the system in different application fields. The author presents results of the enhanced closed-loop verification approach and of the signal quality using the Cuff-electrode-based ENG-data acquisition system.","PeriodicalId":375268,"journal":{"name":"Research Anthology on Emerging Technologies and Ethical Implications in Human Enhancement","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Anthology on Emerging Technologies and Ethical Implications in Human Enhancement","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-7998-8050-9.ch015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Simulation and modelling are powerful methods in computer aided therapy, rehabilitation monitoring, identification and control. The smart modular biosignal acquisition and identification system (SMoBAICS) provides methods and techniques to acquire electromyogram (EMG)- and electroneurogram (ENG)-based data for the evaluation and identification of biosignals. In this paper the author focuses on the development, integration and verification of platform technologies which support this entire data processing. Simulation and verification approaches are integrated to evaluate causal relationships between physiological and bioinformatical processes. Based on this we are stepping up of efforts to develop substitute methods and computer-aided simulation models with the objective of reducing animal testing. This work continues the former work about system identification and biosignal acquisition and verification systems presented in (Bohlmann et al., 2010), (Klinger and Klauke, 2013), (Klinger, 2014). This paper focuses on the next generation of an embedded data acquisition and identification system and its flexible platform architecture. Different application scenarios are shown to illustrate the system in different application fields. The author presents results of the enhanced closed-loop verification approach and of the signal quality using the Cuff-electrode-based ENG-data acquisition system.
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SMoBAICS
仿真和建模是计算机辅助治疗、康复监测、识别和控制的有力方法。智能模块化生物信号采集和识别系统(SMoBAICS)提供了获取基于肌电图(EMG)和神经电图(ENG)的数据的方法和技术,用于评估和识别生物信号。在本文中,作者重点研究了支持整个数据处理的平台技术的开发、集成和验证。模拟和验证方法被整合到评估生理和生物信息过程之间的因果关系。基于此,我们正在加紧努力开发替代方法和计算机辅助模拟模型,以减少动物试验。这项工作延续了(Bohlmann et al., 2010)、(Klinger and Klauke, 2013)、(Klinger, 2014)中提出的关于系统识别和生物信号采集与验证系统的先前工作。本文重点研究了下一代嵌入式数据采集与识别系统及其灵活的平台架构。通过不同的应用场景来说明系统在不同的应用领域。作者介绍了增强闭环验证方法的结果,以及使用基于袖夫电极的eng数据采集系统的信号质量。
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