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Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/411

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Browsing Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu by Subject "Accelerometer"

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    Citation Count: Akan, E.; Akagündüz, E.; Uslu, I. B. (2021). "A data collection system design for hand gestures", 2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedings.
    A data collection system design for hand gestures
    (2021) Akan, E.; Akagündüz, 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 Count: 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.
    Hand Gesture Classification Using Inertial Based Sensors via a Neural Network
    (IEEE, 2017) Akan, Erhan; Tora, Hakan; Uslu, Baran; 251470
    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.