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
. 2024 Feb 1;14(1):2702.
doi: 10.1038/s41598-024-53069-1.

Wireless body area sensor networks based human activity recognition using deep learning

Ehab El-Adawi  1 , Ehab Essa  2 , Mohamed Handosa  1 , Samir Elmougy  3
Affiliations

Wireless body area sensor networks based human activity recognition using deep learning

Ehab El-Adawi et al. Sci Rep. .

Abstract

In the healthcare sector, the health status and biological, and physical activity of the patient are monitored among different sensors that collect the required information about these activities using Wireless body area network (WBAN) architecture. Sensor-based human activity recognition (HAR), which offers remarkable qualities of ease and privacy, has drawn increasing attention from researchers with the growth of the Internet of Things (IoT) and wearable technology. Deep learning has the ability to extract high-dimensional information automatically, making end-to-end learning. The most significant obstacles to computer vision, particularly convolutional neural networks (CNNs), are the effect of the environment background, camera shielding, and other variables. This paper aims to propose and develop a new HAR system in WBAN dependence on the Gramian angular field (GAF) and DenseNet. Once the necessary signals are obtained, the input signals undergo pre-processing through artifact removal and median filtering. In the initial stage, the time series data captured by the sensors undergoes a conversion process, transforming it into 2-dimensional images by using the GAF algorithm. Then, DenseNet automatically makes the processes and integrates the data collected from diverse sensors. The experiment results show that the proposed method achieves the best outcomes in which it achieves 97.83% accuracy, 97.83% F-measure, and 97.64 Matthews correlation coefficient (MCC).

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
HAR Framework adapted from.
Figure 2
Figure 2
The position of the sensor to collect data for the MHEALTH dataset.
Figure 3
Figure 3
The proposed hybrid HAR system in WBAN architecture based on GAF algorithm and DenseNet169 model.
Algorithm 1
Algorithm 1
The steps of the proposed method based on a hybrid GAF + DenseNet169.
Figure 4
Figure 4
Confusion matrix for the model output.
Figure 5
Figure 5
Plots of accuracy curves for the dataset.
See this image and copyright information in PMC

References

    1. Al-Mishmish H, et al. Critical data-based incremental cooperative communication for wireless body area network. Sensors. 2018;18:3661. doi: 10.3390/s18113661. - DOI - PMC - PubMed
    1. Alkhayyat, A., Gazi, O. & Sadkhan, S. B. The role of delay and connectivity in throughput reduction of cooperative decentralized wireless networks. Math. Probl. Eng.2015 (2015).
    1. Liu X, Hu F, Shao M, Sui D, He G. Power allocation for energy harvesting in wireless body area networks. China Commun. 2017;14:22–31. doi: 10.1109/CC.2017.7961360. - DOI
    1. Özyer T, Ak DS, Alhajj R. Human action recognition approaches with video datasets-a survey. Knowl.-Based Syst. 2021;222:106995. doi: 10.1016/j.knosys.2021.106995. - DOI
    1. Zhang Y-D, Satapathy SC, Guttery DS, Górriz JM, Wang S-H. Improved breast cancer classification through combining graph convolutional network and convolutional neural network. Inf. Process. Manag. 2021;58:102439. doi: 10.1016/j.ipm.2020.102439. - DOI

LinkOut - more resources