Abstract
Currently, human-computer interfaces have a number of useful applications for people.The use of electromyographic signals (EMG) has shown to be effective for human-computer interfaces. Theclassification of patterns based on EMG signals has been successfully applied in various tasks such asmotion detection to control of video games. An alternative to increasing access to these applicationsis the use of low-cost hardware to sample the EMG signals considering a real-time response. This paperpresents a methodology for recognizing patterns of EMG signals given by arm movements in real time. Ourproposal is based on an artificial Neural Network, Multilayer Perceptron, where the EMG signals are processedby a set of signal processing techniques. The hardware used for obtaining the signal is based on Ag/AgClconnected to the EKG/EMG-Shield plate mounted on a Arduino One R3 card which is used to control a videogame. The implemented application achieves an accuracy above 90 % using less than 0.2 s for recognitionof actions in time of testing. Our methodology is shown to predict different movements of the human armreliably, at a low cost and in real time.
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Caro, L.A., Silva, C., Peralta, B., Herrera, O.A., Barrientos, S. (2015). Real-Time Recognition of Arm Motion Using Artificial Neural Network Multi-perceptron with Arduino One MicroController and EKG/EMG Shield Sensor. In: Bravo, J., Hervás, R., Villarreal, V. (eds) Ambient Intelligence for Health. AmIHEALTH 2015. Lecture Notes in Computer Science(), vol 9456. Springer, Cham. https://doi.org/10.1007/978-3-319-26508-7_1
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DOI: https://doi.org/10.1007/978-3-319-26508-7_1
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