Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors
| Autor: | Çağatay Berke Erdaş, Selda Guney |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer Networks and Communications business.industry Computer science General Neuroscience Deep learning Frame (networking) Wearable computer Gyroscope 02 engineering and technology Accelerometer Convolutional neural network Task (project management) law.invention Activity recognition 020901 industrial engineering & automation Artificial Intelligence law 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Software |
| Zdroj: | Neural Processing Letters. 53:1795-1809 |
| ISSN: | 1573-773X 1370-4621 |
| DOI: | 10.1007/s11063-021-10448-3 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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