Next Article in Journal
Machine Vibration Monitoring for Diagnostics through Hypothesis Testing
Previous Article in Journal
Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation
Article Menu

Article Versions

Export Article

Open AccessArticle

Asymmetric Residual Neural Network for Accurate Human Activity Recognition

1
School of Computer Science and Engineering, Central South University, Changsha 410083, China
2
Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(6), 203; https://doi.org/10.3390/info10060203
Received: 21 May 2019 / Revised: 31 May 2019 / Accepted: 5 June 2019 / Published: 6 June 2019
(This article belongs to the Special Issue Application of Artificial Intelligence in Sports)
PDF [942 KB, uploaded 6 June 2019]

Abstract

Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.
Keywords: human activity recognition; deep neural network; residual networks; spatial features; temporal features human activity recognition; deep neural network; residual networks; spatial features; temporal features
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Long, J.; Sun, W.; Yang, Z.; Raymond, O.I. Asymmetric Residual Neural Network for Accurate Human Activity Recognition. Information 2019, 10, 203.

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.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top