Abstract
Brain-controlled wheelchair is an assisting device for patients with motor disabilities controlled by brain waves. The user convenience and safety of the brain-controlled wheelchair development using EMG are focused. Patients with disabilities who are still able to move his fingers can control the brain-controlled wheelchair with a finger. This paper discusses the design and implementation of signal processing using artificial neural network for classification of motion command brain-controlled wheelchair. The signal processing is divided into three parts, namely preprocessing, feature extraction, and classification. Preprocessing stage using digital filter, FIR bandpass filter 10–500 Hz, and notch filter at 50 Hz to eliminate noise. The preprocessing proceeds at the characteristic extraction stage in the form of RMS, MAX, VAR, SD, and MAV. The value of the feature will be calculated using the artificial neural network to generate the command such as forward, turn right, turn left, or stop.
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This research was supported by the Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Indonesia.
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Turnip, A., Kusumandari, D.E., Arson, G.W.G., Setiadikarunia, D. (2021). Electric Wheelchair Controlled-Based EMG with Backpropagation Neural Network Classifier. In: Joelianto, E., Turnip, A., Widyotriatmo, A. (eds) Cyber Physical, Computer and Automation System. Advances in Intelligent Systems and Computing, vol 1291. Springer, Singapore. https://doi.org/10.1007/978-981-33-4062-6_13
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DOI: https://doi.org/10.1007/978-981-33-4062-6_13
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