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

被引：0
作者：
Çağatay Berke Erdaş
Selda Güney
机构：
[1] Başkent University,Department of Computer Engineering, Faculty of Engineering
[2] Başkent University,Department of Electrical and Electronics Engineering, Faculty of Engineering
来源：
Neural Processing Letters | 2021年 / 53卷
关键词：
Wearable sensors; Human activity recognition; Deep learning; CNN; Convolutional LSTM;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：1795 / 1809
页数：14
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