SMoBAICS: The Smart Modular Biosignal Acquisition and Identification System for Prosthesis Control and Rehabilitation Monitoring

SMoBAICS: The Smart Modular Biosignal Acquisition and Identification System for Prosthesis Control and Rehabilitation Monitoring

Volkhard Klinger (Department of Embedded Systems, FHDW Hannover, Hannover, Germany)
DOI: 10.4018/IJPHIM.2017070103
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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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1. Introduction

The use of electrical biosignals, like electroencephalogram (EEG), electromyogram (EMG) and electroneurogram (ENG), gain a lot of importance for the assessment of functions in the human body. These signals are used as major indicators for medical professionals, patients or professional athletes during diagnostic and monitoring processes. Furthermore, the biosignal-based intelligent control of prostheses or handicapped limbs is a key challenge in medical technology. In particular EMG and ENG are used to get information about the peripheral nervous system including information transfer due to sense data and motion control by peripheral nerves. Based on these signals a multitude of applications are existing or in the future envisaged; they range from the achievement of therapeutic objectives up to prosthesis control, for example, to operate an artificial hand or forearm. There are several requirements on a system existing to realize these functionalities:

  • Data acquisition and stimulation

    • The EEG, EMG or ENG data has to be acquired and sampled according their signal characteristics, given in Table 1. In particular, additional applications, like a stimulation, are necessary, for example providing the measurement of the nerve conduction velocity.

  • Data processing

    • The acquired data (action potentials) are disturbed by intrinsic noise. In addition, they are overlaid by a substantial extrinsic noise, originated for example by EMG from surrounding muscles. Therefore, we have to filter the recorded data with integrated analog filter and additional digital filter. There are several specific high-pass, low-pass, band-pass and notch filters available. A further data processing is necessary, on the one hand to improve the data condition due to asynchronous and aperiodic samples, and on the other hand to generate events from the action potentials like the activity level of a muscle group or the detection of an exposure scenario.

  • Identification

    • The identification feature is required for prosthesis control or any type of high level signal evaluation and correlation. The identification is based on machine learning and recognizes movement commands and inherent feedback signals (Verdult, 2002), (Wodlinger and Durand, 2011). The identification method and the corresponding verification scenario have been introduced in (Klinger and Klauke, 2013), (Klinger, 2014) based on results in [Bohlmann et al., 2010], [Bohlmann et al., 2011]. In this paper, we focus on the closed-loop verification approach.

  • Data archiving

    • After data acquisition and data processing the results have to be saved locally if there is no direct data transmission for an evaluation possible or if local data are required due to an offline analysis. Furthermore, for identification a certain data amount is necessary during the operating phase (Klinger and Klauke, 2013).

  • Data interfacing

    • Data has to be transmitted for evaluation or monitoring purposes to a host system.

  • User Interfacing

    • To select and execute certain functionalities and for online information an user interface must be available.

  • Configuration

    • Due to the different application scenarios and system functions a configuration is necessary.

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