A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors
Issued Date
2022-05-01
Resource Type
eISSN
20763417
Scopus ID
2-s2.0-85130719415
Journal Title
Applied Sciences (Switzerland)
Volume
12
Issue
10
Rights Holder(s)
SCOPUS
Bibliographic Citation
Applied Sciences (Switzerland) Vol.12 No.10 (2022)
Suggested Citation
Mekruksavanich S., Hnoohom N., Jitpattanakul A. A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors. Applied Sciences (Switzerland) Vol.12 No.10 (2022). doi:10.3390/app12104988 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84091
Title
A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
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.