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A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone

Qi, Wen; Su, Hang; Yang, Chenguang; Ferrigno, Giancarlo; De Momi, Elena; Aliverti, Andrea

A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone Thumbnail


Authors

Wen Qi

Hang Su

Giancarlo Ferrigno

Elena De Momi

Andrea Aliverti



Abstract

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%.

Citation

Qi, W., Su, H., Yang, C., Ferrigno, G., De Momi, E., & Aliverti, A. (2019). A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone. Sensors, 19(17), Article 3731. https://doi.org/10.3390/s19173731

Journal Article Type Article
Acceptance Date Aug 27, 2019
Online Publication Date Aug 29, 2019
Publication Date Sep 1, 2019
Deposit Date Oct 29, 2019
Publicly Available Date Mar 28, 2024
Journal Sensors (Switzerland)
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 19
Issue 17
Article Number 3731
DOI https://doi.org/10.3390/s19173731
Keywords Electrical and Electronic Engineering; Analytical Chemistry; Atomic and Molecular Physics, and Optics; Biochemistry
Public URL https://uwe-repository.worktribe.com/output/4192010

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