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dc.contributor.author Birur, Sushmalekha Shankar
dc.date.accessioned 2018-06-12T22:15:08Z
dc.date.available 2018-06-12T22:15:08Z
dc.date.issued 2018
dc.identifier.uri http://hdl.handle.net/10211.3/203771
dc.description.abstract Gesture recognition is one of the revolutionary technological advancements seen in human-computer interaction field today. The applications of gesture recognition are vast from sign language recognition, through prosthesis control, to control interface for virtual reality (VR) and augmented reality (AR) systems. Many of these applications use supervised learning-based machine learning algorithms to interpret data and make decisions. Unsupervised learning is still in the early development stage since it involves streaming in continuous unlabeled data sets and obtaining meaningful data sets out of billions of data remains a challenge. This project aims to develop and analyze k-means clustering - an unsupervised learning technique, for hand gesture recognition based on unlabeled electromyographic (EMG) data sets obtained from a commercial armband Myo developed by Thalmic Labs. en_US
dc.format.extent x, 57 leaves en_US
dc.language.iso en_US en_US
dc.publisher San Francisco State University en_US
dc.rights Copyright by Sushmalekha Shankar Birur, 2018 en_US
dc.source AS36 2018 ENGR .B57
dc.title Hand gesture recognition using unsupervised learning en_US
dc.type Thesis en_US
dc.contributor.department Engineering en_US

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