Akan, ErhanAkan,E.Tora, HakanTora,H.Uslu,B.Airframe and Powerplant MaintenanceDepartment of Electrical & Electronics Engineering2024-07-052024-07-0520179978-153861911-710.1109/ICECS.2017.82920742-s2.0-85047266021https://doi.org/10.1109/ICECS.2017.8292074https://hdl.handle.net/20.500.14411/3826In 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.eninfo:eu-repo/semantics/closedAccessaccelerometergesture recognitiongyroscopemagnetometerneural networkorientation sensorConference Object2018-January140143