Toward Data Augmentation and Interpretation in Sensor-Based Fine-Grained Hand Activity Recognition

Conference Paper

Recognizing fine-grained hand activities has widely attracted the research community’s attention in recent years. However, rather than enriched sen-sor-based datasets of whole-body activities, there are limited data available for acceler-ator-based fine-grained hand activities. In this paper, we propose a purely convolution-based Generative Adversarial Networks (GAN) approach for data augmentation on accelerator-based temporal data of fine-grained hand activities. The approach consists of 2D-Convolution discriminator and 2D-Transposed-Convolution generator that are shown capable of learning the distribution of re-shaped sensor-based data and generating synthetic instances that well reserve the cross-axis co-relation. We evaluate the usability of synthetic data by expanding existing datasets and improving the state-of-the-art classifier’s test accuracy. The in-nature unreadable sensor-based data is interpreted by introducing visualization methods including axis-wise heatmap and model-oriented decision explanation. The experiments show that our approach can effectively improve the classifier’s test accuracy by GAN-based data augmentation while well preserving the authenticity of synthetic data.

Full Text

Duke Authors

Cited Authors

  • Luo, J; Li, X; Younes, R

Published Date

  • January 1, 2021

Published In

Volume / Issue

  • 1370 /

Start / End Page

  • 30 - 42

Electronic International Standard Serial Number (EISSN)

  • 1865-0937

International Standard Serial Number (ISSN)

  • 1865-0929

International Standard Book Number 13 (ISBN-13)

  • 9789811605741

Digital Object Identifier (DOI)

  • 10.1007/978-981-16-0575-8_3

Citation Source

  • Scopus