Title

Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks

Abstract

Human physical activity recognition based on wearable sen-sors has applications relevant to our daily life such as health-care. How to achieve high recognition accuracy with low computational cost is an important issue in the ubiquitous computing. Rather than exploring handcrafted features from time-series sensor signals, we assemble signal sequences of accelerometers and gyroscopes into a novel activity image, which enables Deep Convolutional Neural Networks (DCNN) to automatically learn the optimal features from the activ-ity image for the activity recognition task. Our proposed approach is evaluated on three public datasets and it out-performs state-of-The-Arts in terms of recognition accuracy and computational cost.

Meeting Name

2015 ACM Multimedia Conference MM'15 (2017: Oct. 26-30, Brisbane, Australia)

Department(s)

Computer Science

Keywords and Phrases

Convolution; Image Recognition; Neural Networks; Pattern Recognition; Ubiquitous Computing; Wearable Technology; Activity Recognition; Computational Costs; Convolutional Neural Network; Human Activity Recognition; Physical Activity; Recognition Accuracy; State of the Art; Wearable Computing; Wearable Sensors; activity Image; Deep Convolutional Neural Networks; Wearable Computing

International Standard Book Number (ISBN)

9781450334594

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Association for Computing Machinery (ACM), All rights reserved.

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