Off-the-shelf electronic market is large and diverse. It includes credit card size computers (example: Raspberry Pi) or microcontroller boards (example: Arduino) that are relatively low cost, open source and easy to use. Nevertheless, there is a lack of off-the-shelf, open source devices that would enable us to learn about and make use of physiological signal processing. An example of such a device is an electromyograph (EMG).
In this thesis, we investigated if an EMG device could fulfil the afore mentioned gap. EMG device was a five channel open source Arduino EMG shield. The performance of the device was evaluated on three healthy male subjects aged 15, 22, 27 (age = 21 ± 6(SD) years). They were instructed to perform simple finger movements, which we classified and replicated 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 %. Our results confirm, that by using a trained movement classifier based on the support vector machine algorithm we can control a robotic hand in real time by utilising the EMG signal acquired by an Arduino EMG shield. We achieved our results by using a cost effective and customisable device holding the potential to provide access to easier human-machine interface prototyping and learning about neurophysiology.
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