Next Article in Journal
Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture
Next Article in Special Issue
ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold
Previous Article in Journal
Detonation Velocity Measurement with Chirped Fiber Bragg Grating
Previous Article in Special Issue
Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging
Open AccessArticle

Deep Recurrent Neural Networks for Human Activity Recognition

Department of Information Communication Engineering, Chosun University, 375 Susuk-dong, Dong-gu, Gwangju 501-759, Korea
Author to whom correspondence should be addressed.
Sensors 2017, 17(11), 2556;
Received: 18 October 2017 / Revised: 3 November 2017 / Accepted: 3 November 2017 / Published: 6 November 2017
(This article belongs to the Special Issue Sensor Signal and Information Processing)
Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs. View Full-Text
Keywords: human activity recognition; deep learning; recurrent neural networks human activity recognition; deep learning; recurrent neural networks
Show Figures

Graphical abstract

MDPI and ACS Style

Murad, A.; Pyun, J.-Y. Deep Recurrent Neural Networks for Human Activity Recognition. Sensors 2017, 17, 2556.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop