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Hand Gesture Classification Using Inertial Based Sensors via a Neural Network

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dc.contributor.author Akan, Erhan
dc.contributor.author Tora, Hakan
dc.contributor.author Uslu, Baran
dc.date.accessioned 2020-12-01T07:49:06Z
dc.date.available 2020-12-01T07:49:06Z
dc.date.issued 2017
dc.identifier.citation Akan, Erhan; Tora, Hakan; Uslu, Baran. "Hand Gesture Classification Using Inertial Based Sensors via a Neural Network", Electronics, Circuits and Systems (ICECS), pp. 1-4, 2017. tr_TR
dc.identifier.uri http://hdl.handle.net/20.500.12416/4289
dc.description.abstract In this study, a mobile phone equipped with four types of sensors namely, accelerometer, gyroscope, magnetometer and orientation, is used for gesture classification. Without feature selection, the raw data from the sensor outputs are processed and fed into a Multi-Layer Perceptron classifier for recognition. The user independent, single user dependent and multiple user dependent cases are all examined. Accuracy values of 91.66% for single user dependent case, 87.48% for multiple user dependent case and 60% for the user independent case are obtained. In addition, performance of each sensor is assessed separately and the highest performance is achieved with the orientation sensor. tr_TR
dc.language.iso eng tr_TR
dc.publisher IEEE tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Gesture Recognition tr_TR
dc.subject Neural Network tr_TR
dc.subject Accelerometer tr_TR
dc.subject Magnetometer tr_TR
dc.subject Gyroscope tr_TR
dc.subject Orientation Sensor tr_TR
dc.title Hand Gesture Classification Using Inertial Based Sensors via a Neural Network tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Electronics, Circuits and Systems (ICECS) tr_TR
dc.contributor.authorID 251470 tr_TR
dc.identifier.startpage 1 tr_TR
dc.identifier.endpage 4 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


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