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A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors

A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors

Authors :
Wenxian Yu
Fanyi Xiao
Lei Chu
Danping Zou
Tao Li
Ling Pei
Yifan Zhu
Source :
Spatial Data and Intelligence ISBN: 9783030698720, SpatialDI
Publication Year :
2021
Publisher :
Springer International Publishing, 2021.

Abstract

Sensor-based human activity recognition (HAR) is now a research hotspot in multiple application areas. With the rise of smart wearable devices equipped with inertial measurement units (IMUs), researchers begin to utilize IMU data for HAR. By employing machine learning algorithms, early IMU-based research for HAR can achieve accurate classification results on traditional classical HAR datasets, containing only simple and repetitive daily activities. However, these datasets rarely display a rich diversity of information in real-scene. In this paper, we propose a novel method based on deep learning for complex HAR in the real-scene. Specially, in the off-line training stage, the AMASS dataset, containing abundant human poses and virtual IMU data, is innovatively adopted for enhancing the variety and diversity. Moreover, a deep convolutional neural network with an unsupervised penalty is proposed to automatically extract the features of AMASS and improve the robustness. In the on-line testing stage, by leveraging advantages of the transfer learning, we obtain the final result by fine-tuning the partial neural network (optimizing the parameters in the fully-connected layers) using the real IMU data. The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91.15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.

Details

ISBN :
978-3-030-69872-0
ISBNs :
9783030698720
Database :
OpenAIRE
Journal :
Spatial Data and Intelligence ISBN: 9783030698720, SpatialDI
Accession number :
edsair.doi...........b7b461b352968ba6d7d01bda3b08a1b7
Full Text :
https://doi.org/10.1007/978-3-030-69873-7_19