Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Mar 31;19(7):1556.
doi: 10.3390/s19071556.

Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition

Carlos Avilés-Cruz  1 , Andrés Ferreyra-Ramírez  2 , Arturo Zúñiga-López  3 , Juan Villegas-Cortéz  4
Affiliations

Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition

Carlos Avilés-Cruz et al. Sensors (Basel). .

Abstract

In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. This paper presents a novel framework to classify and analyze human activities. A new convolutional neural network (CNN) strategy is applied to a single user movement recognition using a smartphone. Three parallel CNNs are used for local feature extraction, and latter they are fused in the classification task stage. The whole CNN scheme is based on a feature fusion of a fine-CNN, a medium-CNN, and a coarse-CNN. A tri-axial accelerometer and a tri-axial gyroscope sensor embedded in a smartphone are used to record the acceleration and angle signals. Six human activities successfully classified are walking, walking-upstairs, walking-downstairs, sitting, standing and laying. Performance evaluation is presented for the proposed CNN.

Keywords: CNN; classification; deep-learning; human action recognition.

PubMed Disclaimer

Conflict of interest statement

The authors declare that there is no conflict of interests regarding the publication of this paper.

Figures

Figure 1
Figure 1
Proposed Coarse-fine convolutional neural network topology.
Figure 2
Figure 2
Partial CNN proposed architecture.
Figure 3
Figure 3
Example of four human activities: walking, walking-upstairs, and walking-downstairs.
Figure 4
Figure 4
Dependency between the size of the convolutional filters and CNN accuracy.
Figure 5
Figure 5
Training results for the proposed CNN architecture.
Figure 6
Figure 6
Training-validation results for the proposed CNN architecture.
Figure 7
Figure 7
Testing results for the proposed CNN architecture.
Figure 8
Figure 8
Performance comparison against the most competitive methods: W->walking, WU->walking-upstairs, WD->walking-downstairs, S->sitting, ST->standing and L->laying.
See this image and copyright information in PMC

References

    1. Lara O., Labrador M. A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 2013;15:1192–1209. doi: 10.1109/SURV.2012.110112.00192. - DOI
    1. Chen L., Hoey J., Nugent C., Cook D., Yu Z. Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2012;42:790–808. doi: 10.1109/TSMCC.2012.2198883. - DOI
    1. Anguita D., Ghio A., Oneto L., Parra X., Reyes-Ortiz J.L. A Public Domain Dataset for Human Activity Recognition Using Smartphones; Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013); Bruges, Belgium. 24–26 April 2013;
    1. Le T.D., Nguyen C.V. Human Activity Recognition by smartphone; Proceedings of the 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science; Ho Chi Minh City, Vietnam. 16–18 September 2015; pp. 219–224.
    1. Liang Y., Zhou X., YU Z., Guo B. Energy-Efficient Motion Related Activity Recognition on Mobile Devices for Pervasive Healthcare. Mob. Netw. Appl. 2014;19:303–317. doi: 10.1007/s11036-013-0448-9. - DOI

LinkOut - more resources