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Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors

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Abstract

With the spread of wearable sensors, the solutions to the task of activity recognition by using the data obtained from the sensors have become widespread. Recognition of activities owing to wearable sensors such as accelerometers, gyroscopes, and magnetometers, etc. has been studied in recent years. Although there are several applications in the literature, differently in this study, deep learning algorithms such as Convolutional Neural Networks, Convolutional LSTM, and 3D Convolutional Neural Networks fed by Convolutional LSTM have been used in human activity recognition task by feeding with data obtained from accelerometer sensor. For this purpose, a frame was formed with raw samples of the same activity which were collected consecutively from the accelerometer sensor. Thus, it is aimed to capture the pattern inherent in the activity and due to preserving the continuous structure of the movement.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Başkent University, Ankara, Turkey

    Çağatay Berke Erdaş

  2. Department of Electrical and Electronics Engineering, Faculty of Engineering, Başkent University, Ankara, Turkey

    Selda Güney

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  1. Çağatay Berke Erdaş
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  2. Selda Güney
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Correspondence to Çağatay Berke Erdaş.

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Erdaş, Ç.B., Güney, S. Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors. Neural Process Lett 53, 1795–1809 (2021). https://doi.org/10.1007/s11063-021-10448-3

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