Home // SPWID 2018, The Fourth International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems // View article
Authors:
Patricio Rivera Lopez
Edwin Valarezo Añazco
Sangmin Lee
Kyung Min Byun
Min Hyoung Cho
Soo Yeol Lee
Tae-Seong Kim
Keywords: Human Hand Activity Recognition; Autoencoder; Deep Learning; RNN; CNN.
Abstract:
Human Hand Activity Recognition (HAR) using wearable sensors can be utilized in various practical applications such as lifelogging, human-computer interaction, and gesture interfaces. Especially with the latest deep learning approaches, the feasibility of HAR in practice gets more promising. In this paper, we present a HAR system based on deep Autoencoder for denoising and deep Recurrent Neural Network (RNN) for classification. The proposed HAR system achieves a mean accuracy of 79.38% for seven complex hand activities, while only of 72.65% without the autoencoder. The presented combination of autoencoder and RNN could be useful for much improved human activity recognition.
Pages: 3 to 7
Copyright: Copyright (c) IARIA, 2018
Publication date: July 22, 2018
Published in: conference
ISSN: 2519-8440
ISBN: 978-1-61208-657-6
Location: Barcelona, Spain
Dates: from July 22, 2018 to July 26, 2018