Gesture-based human machine interaction using RCNNs in limited computation power devices

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dc.contributor.authorTellaeche Iglesias, Alberto
dc.contributor.authorFidalgo Astorquia, Ignacio
dc.contributor.authorVazquez, Juan-Ignacio
dc.contributor.authorSaikia, Surajit
dc.date.accessioned2025-06-20T07:18:24Z
dc.date.available2025-06-20T07:18:24Z
dc.date.issued2021-12-08
dc.date.updated2025-06-20T07:18:24Z
dc.description.abstractThe use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.en
dc.identifier.citationTellaeche Iglesias, A., Fidalgo Astorquia, I., Vázquez Gómez, J. I., & Saikia, S. (2021). Gesture-based human machine interaction using RCNNs in limited computation power devices. Sensors (Basel, Switzerland), 21(24). https://doi.org/10.3390/S21248202
dc.identifier.doi10.3390/S21248202
dc.identifier.eissn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3113
dc.language.isoeng
dc.publisherNLM (Medline)
dc.rights© 2021 by the authors
dc.subject.otherDeep learning
dc.subject.otherEmbedded systems
dc.subject.otherGesture detection
dc.subject.otherReal time
dc.titleGesture-based human machine interaction using RCNNs in limited computation power devicesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue24
oaire.citation.titleSensors (Basel, Switzerland)
oaire.citation.volume21
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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