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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
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Sensors 2017, 17(11), 2556; https://doi.org/10.3390/s17112556
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
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MDPI and ACS Style

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

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