@inproceedings{57109e2f11f248a7aa046ce30ca074aa,
title = "A Comparison of Unidirectional and Bidirectional LSTM Networks for Human Activity Recognition",
abstract = "Human activity recognition targets identifying different classes of human movements using data gathered from various types of sensors. Deep learning approaches, such as Recurrent Neural Networks, are gaining interest in the classification of human activities using time series data. Long-Short Term Memory is a recurrent neural network approach that is well suited for the classification of time series data where it handles the vanishing gradient and the long-term dependency problems efficiently. In this paper, we compare the human activity recognition accuracy of the unidirectional and bidirectional Long-Short Term Memory models on two different datasets that represent accelerometer data. The results show that the bidirectional approach slightly enhances the recognition quality over the unidirectional approach. However, the bidirectional approach spends more time during the training, which may hinder its applicability on large datasets.",
keywords = "Accelerometers, Classification, Deep Learning, Recurrent Neural Networks (RNN)",
author = "Luay Alawneh and Belal Mohsen and Mohammad Al-Zinati and Ahmed Shatnawi and Mahmoud Al-Ayyoub",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 18th IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 ; Conference date: 23-03-2020 Through 27-03-2020",
year = "2020",
month = mar,
doi = "10.1109/PerComWorkshops48775.2020.9156264",
language = "English",
series = "2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020",
address = "United States",
}