Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network
| dc.authorscopusid | 57202129682 | |
| dc.authorscopusid | 6506642154 | |
| dc.authorscopusid | 16231740900 | |
| dc.contributor.author | Akan,E. | |
| dc.contributor.author | Tora,H. | |
| dc.contributor.author | Uslu,B. | |
| dc.contributor.other | Airframe and Powerplant Maintenance | |
| dc.contributor.other | Department of Electrical & Electronics Engineering | |
| dc.date.accessioned | 2024-07-05T15:44:47Z | |
| dc.date.available | 2024-07-05T15:44:47Z | |
| dc.date.issued | 2017 | |
| dc.department | Atılım University | en_US |
| dc.department-temp | Akan E., Electrical and Electronics Engineering, Atilim University, Ankara, Turkey; Tora H., Avionics/Electrical and Electronics Engineering, Atilim University, Ankara, Turkey; Uslu B., Electrical and Electronics Engineering, Atilim University, Ankara, Turkey | en_US |
| 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. © 2017 IEEE. | en_US |
| dc.identifier.citationcount | 9 | |
| dc.identifier.doi | 10.1109/ICECS.2017.8292074 | |
| dc.identifier.endpage | 143 | en_US |
| dc.identifier.isbn | 978-153861911-7 | |
| dc.identifier.scopus | 2-s2.0-85047266021 | |
| dc.identifier.startpage | 140 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ICECS.2017.8292074 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14411/3826 | |
| dc.identifier.volume | 2018-January | en_US |
| dc.institutionauthor | Akan, Erhan | |
| dc.institutionauthor | Tora, Hakan | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems -- 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 -- 5 December 2017 through 8 December 2017 -- Batumi -- 134675 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.scopus.citedbyCount | 10 | |
| dc.subject | accelerometer | en_US |
| dc.subject | gesture recognition | en_US |
| dc.subject | gyroscope | en_US |
| dc.subject | magnetometer | en_US |
| dc.subject | neural network | en_US |
| dc.subject | orientation sensor | en_US |
| dc.title | Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
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