Date of Graduation
Computer Science and Computer Engineering
Committee Member/Second Reader
Committee Member/Third Reader
Machine learning has proven to be an effective tool for forming models to make predictions based on sample data. Supervised learning, a subset of machine learning, can be used to map input data to output labels based on pre-existing paired data. Datasets for machine learning can be created from many different sources and vary in complexity, with popular datasets including the MNIST handwritten dataset and CIFAR10 image dataset. The focus of this thesis is to test and validate multiple machine learning models for accurately classifying gestures performed on a low-cost capacitive sensing array. Multiple neural networks are trained using gesture datasets obtained from the capacitance board. In this paper, I train and compare different machine learning models on recognizing gesture datasets. Learning hyperparameters are also adjusted for results. Two datasets are used for the training: one containing simple gestures and another containing more complicated gestures. Accuracy and loss for the models are calculated and compared to determine which models excel at recognizing performed gestures.
machine learning, capacitive sensing, gesture recognition
Fahr Jr., M. (2020). Investigating Machine Learning Techniques for Gesture Recognition with Low-Cost Capacitive Sensing Arrays. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/82