Bidirectional Gated Recurrent Units For Human Activity Recognition Using Accelerometer Data
| Autor: | Luay Alawneh, Mohammad Al-Zinati, Mahmoud Al-Ayyoub, Tamam Alsarhan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Domain (software engineering) Activity recognition Recurrent neural network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Accelerometer data business |
| Zdroj: | 2019 IEEE SENSORS. |
| DOI: | 10.1109/sensors43011.2019.8956560 |
| Popis: | Human activity recognition aims to detect the type of human movement based on sensor data gathered during human activity. Time series classification using deep learning approaches offers opportunities to avoid intensive handcrafted feature extraction techniques where the efficiency and the accuracy are heavily dependent on the quality of variables defined by domain experts. In this paper, we apply recurrent neural networks on data collected from mobile phone accelerometers for the recognition of human activity. More specifically, we use the bidirectional gated recurrent units mechanism. The results show that this technique is promising and provides high quality recognition results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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