Wrapper-Based Deep Feature Optimization for Activity Recognition in the Wearable Sensor Networks of Healthcare Systems

Authors

Karam Kumar Sahoo
Raghunath Ghosh
Saurav Mallik
Arup Roy
Pawan Kumar Singh
Zhongming Zhao

Publication Date

1-18-2023

Journal

Scientific Reports

Abstract

The Human Activity Recognition (HAR) problem leverages pattern recognition to classify physical human activities as they are captured by several sensor modalities. Remote monitoring of an individual's activities has gained importance due to the reduction in travel and physical activities during the pandemic. Research on HAR enables one person to either remotely monitor or recognize another person's activity via the ubiquitous mobile device or by using sensor-based Internet of Things (IoT). Our proposed work focuses on the accurate classification of daily human activities from both accelerometer and gyroscope sensor data after converting into spectrogram images. The feature extraction process follows by leveraging the pre-trained weights of two popular and efficient transfer learning convolutional neural network models. Finally, a wrapper-based feature selection method has been employed for selecting the optimal feature subset that both reduces the training time and improves the final classification performance. The proposed HAR model has been tested on the three benchmark datasets namely, HARTH, KU-HAR and HuGaDB and has achieved 88.89%, 97.97% and 93.82% respectively on these datasets. It is to be noted that the proposed HAR model achieves an improvement of about 21%, 20% and 6% in the overall classification accuracies while utilizing only 52%, 45% and 60% of the original feature set for HuGaDB, KU-HAR and HARTH datasets respectively. This proves the effectiveness of our proposed wrapper-based feature selection HAR methodology.

Keywords

Humans, Human Activities, Neural Networks, Computer, Smartphone, Wearable Electronic Devices, Delivery of Health Care, Health care, Engineering, Mathematics and computing

DOI

10.1038/s41598-022-27192-w

PMID

36653370

PMCID

PMC9846703

PubMedCentral® Posted Date

1-18-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes