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Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters
dc.creator | Poularakis S., Katsavounidis I. | en |
dc.date.accessioned | 2023-01-31T09:50:33Z | |
dc.date.available | 2023-01-31T09:50:33Z | |
dc.date.issued | 2016 | |
dc.identifier | 10.1109/TCYB.2015.2464195 | |
dc.identifier.issn | 21682267 | |
dc.identifier.uri | http://hdl.handle.net/11615/78333 | |
dc.description.abstract | In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system's response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems. © 2013 IEEE. | en |
dc.language.iso | en | en |
dc.source | IEEE Transactions on Cybernetics | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940702150&doi=10.1109%2fTCYB.2015.2464195&partnerID=40&md5=6ba9fafdb6d93edfd43e0d73acbb77f2 | |
dc.subject | Embedded systems | en |
dc.subject | Time delay | en |
dc.subject | Computational advantages | en |
dc.subject | Hand-gesture recognition | en |
dc.subject | Low power embedded systems | en |
dc.subject | Nearest neighbors | en |
dc.subject | NN-based approach | en |
dc.subject | Noisy environment | en |
dc.subject | Recognition accuracy | en |
dc.subject | Trajectory classification | en |
dc.subject | Gesture recognition | en |
dc.subject | accelerometry | en |
dc.subject | algorithm | en |
dc.subject | automated pattern recognition | en |
dc.subject | gesture | en |
dc.subject | hand | en |
dc.subject | human | en |
dc.subject | physiology | en |
dc.subject | procedures | en |
dc.subject | sign language | en |
dc.subject | Accelerometry | en |
dc.subject | Algorithms | en |
dc.subject | Gestures | en |
dc.subject | Hand | en |
dc.subject | Humans | en |
dc.subject | Pattern Recognition, Automated | en |
dc.subject | Sign Language | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters | en |
dc.type | journalArticle | en |
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