Next Article in Journal
Screen-Printed Sensors for Colorimetric Detection of Hydrogen Sulfide in Ambient Air
Previous Article in Journal
Real-Time Thermal Modulation of High Bandwidth MOX Gas Sensors for Mobile Robot Applications
Article Menu
Issue 5 (March-1) cover image

Export Article

Open AccessArticle

SynSys: A Synthetic Data Generation System for Healthcare Applications

School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(5), 1181; https://doi.org/10.3390/s19051181
Received: 16 December 2018 / Revised: 13 February 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
(This article belongs to the Section Intelligent Sensors)
  |  
PDF [1322 KB, uploaded 8 March 2019]
  |  

Abstract

Creation of realistic synthetic behavior-based sensor data is an important aspect of testing machine learning techniques for healthcare applications. Many of the existing approaches for generating synthetic data are often limited in terms of complexity and realism. We introduce SynSys, a machine learning-based synthetic data generation method, to improve upon these limitations. We use this method to generate synthetic time series data that is composed of nested sequences using hidden Markov models and regression models which are initially trained on real datasets. We test our synthetic data generation technique on a real annotated smart home dataset. We use time series distance measures as a baseline to determine how realistic the generated data is compared to real data and demonstrate that SynSys produces more realistic data in terms of distance compared to random data generation, data from another home, and data from another time period. Finally, we apply our synthetic data generation technique to the problem of generating data when only a small amount of ground truth data is available. Using semi-supervised learning we demonstrate that SynSys is able to improve activity recognition accuracy compared to using the small amount of real data alone. View Full-Text
Keywords: Synthetic data; hidden Markov models; regression; smart homes; healthcare data; activity recognition Synthetic data; hidden Markov models; regression; smart homes; healthcare data; activity recognition
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Dahmen, J.; Cook, D. SynSys: A Synthetic Data Generation System for Healthcare Applications. Sensors 2019, 19, 1181.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top