Can I read A Hybrid Network Based on Dense Connection and Weighted Feature Aggregation for Human Activity Recognition on EtoBox?

A Hybrid Network Based on Dense Connection and Weighted Feature Aggregation for Human Activity Recognition by Lv, Tianqi; Wang, Xiaojuan; Jin, Lei; Xiao, Yabo; Song, Mei is a Engineering article available to read on EtoBox.

What is A Hybrid Network Based on Dense Connection and Weighted Feature Aggregation for Human Activity Recognition about?

Human activity recognition (HAR) using body-worn sensors is an active research area in human-computer interaction and human activity analysis. The traditional methods use hand-crafted features to classify multiple activities, which is both heavily dependent on human domain knowledge and results in shallow feature extraction. Rapid developments in deep learning have caused most researchers to switch to deep learning methods, which extract features from raw data automatically. Most of the existing works on human activity recognition tasks involve multimodal sensor data, and these networks mainly focus on the top representation extracted from bottom-up feedforward process without reusing other features from bottom layers. In this paper, we present a novel hybrid deep learning network for human activity recognition that also employs multimodal sensor data; however, our proposed model is a ConvLSTM pipeline that makes full use of the information in each layer extracted along the temporal domain. Thus, we propose a dense connection module (DCM) to ensure maximum information flow between the network layers. Furthermore, we employ a multilayer feature aggregation module (MFAM) to extract f

Who reads A Hybrid Network Based on Dense Connection and Weighted Feature Aggregation for Human Activity Recognition?

Author

Lv, Tianqi; Wang, Xiaojuan; Jin, Lei; Xiao, Yabo; Song, Mei

Publisher

Institute of Electrical and Electronics Engineers; Institute of Electrical and Electronics Engineers Inc.; Institute of Electrical and Electronics Engineers (IEEE); Research Square (ISSN 2169-3536)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)