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Asymmetric Residual Neural Network for Accurate Human Activity Recognition

School of Computer Science and Engineering, Central South University, Changsha 410083, China
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;
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 [977 KB, uploaded 24 June 2019]


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. View Full-Text
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

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

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