A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors

Applied Sciences

Sakorn Mekruksavanich¹  Anuchit Jitpattanakul²  Narit Hnoohom³ 

[1] Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao 56000, Thailand;Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand;Image Information and Intelligence Laboratory, Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand;

关键词: deep residual network;    human activity recognition;    transitional activities;    hybrid deep learning model;    bidirectional GRUs;   

DOI  :  10.3390/app12104988

来源: DOAJ

【 摘 要 】

Numerous learning-based techniques for effective human behavior identification have emerged in recent years. These techniques focus only on fundamental human activities, excluding transitional activities due to their infrequent occurrence and short period. Nevertheless, postural transitions play a critical role in implementing a system for recognizing human activity and cannot be ignored. This study aims to present a hybrid deep residual model for transitional activity recognition utilizing signal data from wearable sensors. The developed model enhances the ResNet model with hybrid Squeeze-and-Excitation (SE) residual blocks combining a Bidirectional Gated Recurrent Unit (BiGRU) to extract deep spatio-temporal features hierarchically, and to distinguish transitional activities efficiently. To evaluate recognition performance, the experiments are conducted on two public benchmark datasets (HAPT and MobiAct v2.0). The proposed hybrid approach achieved classification accuracies of 98.03% and 98.92% for the HAPT and MobiAct v2.0 datasets, respectively. Moreover, the outcomes show that the proposed method is superior to the state-of-the-art methods in terms of overall accuracy. To analyze the improvement, we have investigated the effects of combining SE modules and BiGRUs into the deep residual network. The findings indicates that the SE module is efficient in improving transitional activity recognition.

【 授权许可】

Unknown