Browsing by Author "Akan,E."

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    A Data Collection System Design for Hand Gestures
    (Institute of Electrical and Electronics Engineers Inc., 2021) Akan,E.; Akagunduz,E.; Uslu,I.B.
    In this study, we aim at designing a smart glove, which consists of different inertial sensors and an EMG sensor and developing a human-machine interaction application by pre-processing and fusing these different sensory data. We also aim at providing solutions in cases where image processing-based approaches are inefficient. In the proposed smart glove, the quaternion-based orientation data to be produced by the magnetometer and gyroscope together, the acceleration data to be generated by the accelerometer, and the analog data generated by the EMG sensor are collected and then prepared for use by different applications. © 2021 IEEE.
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    Citation - Scopus: 11
    Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2017) Akan,E.; Tora,H.; Uslu,B.
    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.