Araştırma Çıktıları / Research Outputs

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Browsing Araştırma Çıktıları / Research Outputs by Author "Akan, Erhan"

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    Conference Object
    Citation Count: 0
    A data collection system design for hand gestures
    (Institute of Electrical and Electronics Engineers Inc., 2021) Akan,E.; Akagunduz,E.; Uslu,I.B.; Department of Electrical & Electronics Engineering
    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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    Master Thesis
    El hareketleri için bir veri toplama sistemi tasarımı
    (2019) Akan, Erhan; Uslu, İbrahim Baran; Akagündüz, Erdem; Department of Electrical & Electronics Engineering
    Bu çalışmada, bir akıllı eldiven tasarımının yapılması, eldiven üzerindeki farklı ataletsel sensörler ve EMG sensörden veri toplanması, bu verilerin ön işlemeye tabi tutulması ve bu farklı sensör verilerinin kaynaştırılması yoluyla bir insan-makine etkileşimi uygulamasının geliştirilmesi amaçlanmaktadır. Böylelikle görüntü işleme temelli yaklaşımların kusurlu olduğu noktalarda çözümler sunulması hedeflenmektedir. Akıllı eldivende, manyetometre ve jiroskop tarafından üretilecek olan dördey bazlı oryantasyon verileri ile ivmeölçer tarafından üretilecek olan ivme verilerinin ve EMG Sensor tarafından üretilen analog verilerin, toplanması ve daha sonradan farklı uygulamalarca kullanılmasına hazırlık konusunda bir çalışma yapılmıştır.
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    Conference Object
    Citation Count: 9
    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.; Airframe and Powerplant Maintenance; Department of Electrical & Electronics Engineering
    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.
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    Citation Count: 7
    Hand Gesture Classification Using Inertial Based Sensors via a Neural Network
    (Ieee, 2017) Akan, Erhan; Tora, Hakan; Uslu, Baran; Airframe and Powerplant Maintenance; Department of Electrical & Electronics Engineering
    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.