Abstract
Off-the-shelf electronic market is large, diverse and easily accessible by many. Credit card size computers (example: Raspberry Pi) or micro-controller boards (example: Arduino) can be used for learning how to code and how to control embedded systems. Nevertheless, there is a lack of off-the-shelf, open source devices that would enable us to learn about and make use of human signal processing. An example of such a device is an electromyograph (EMG). In this paper we investigated, if an EMG device could fulfill the aforementioned gap. EMG device we used for conducting our experiment was a five channel open source EMG Arduino shield. The performance of the device was evaluated on three healthy male subjects. They were instructed to perform basic finger movements which we classified and executed on the robotic hand. The EMG signal classification was performed using a Support Vector Machine (SVM) algorithm. In our experimental setup the average EMG signal classification accuracy was 78.29%. This we believe demonstrates there are EMG devices on the market today that provide access to cost effective prototyping and learning about EMG signals.
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Cevzar, M., Petrič, T., Babič, J. (2018). Open Source EMG Device for Controlling a Robotic Hand. In: Ferraresi, C., Quaglia, G. (eds) Advances in Service and Industrial Robotics. RAAD 2017. Mechanisms and Machine Science, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-61276-8_84
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DOI: https://doi.org/10.1007/978-3-319-61276-8_84
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