Abstract
Performing hand gestures is a form of nonverbal communication that occurs every day all over the world. The act of opening a door requires the hand to clench in a fist around the handle. Some may motion an individual to stop in place by flexing their hand upward with their palm facing away from the performer. Many individuals are used to performing hand gestures like these without much thought, but not everyone is this fortunate. Individuals who have lost their hand for one reason or the other are limited in their nonverbal communication capabilities. To address this, medical professionals develop controllable prosthetics driven by trained models to perform hand gestures and restore biological accurate movement. This paper aims to develop a genetic fuzzy classifier that uses myoelectric signals acquired by electromyographic sensors to characterize certain movements of an individual’s hand. The eight sensor readings are preprocessed before becoming inputs to the genetic fuzzy system. The output from the system is a single hand gesture. The genetic fuzzy classifier displays an accuracy of 67% using the training dataset and 69% using the testing set while classifying 3 hand gestures.
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The authors wish to thank Lynn Pickering, Javier Viaña, and Noah Glaser for their suggestions on preparing this manuscript.
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Palmer, H., Cohen, K. (2022). Genetic Fuzzy Hand Gesture Classifier. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_30
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