Name
Title | ||
|---|---|---|
Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach. |
Abstract | ||
|---|---|---|
Recent advancements in mobile devices, data analysis, and wearable sensors render the capability of in-place health monitoring. Supervised machine learning algorithms, the core intelligence of these systems, learn from labeled training data. However, labeling vast amount of data is time-consuming and expensive. Moreover, sensor data often contains personal information that a user may not be comfortable sharing. Therefore, there is a strong need to develop methods for generating realistic labeled sensor data. In this paper, we propose a supervised generative adversarial network architecture that learns from feedback from both a discriminator and a classifier in order to create synthetic sensor data. We demonstrate the effectiveness of the architecture on a publicly available human activity dataset. We show that our generator learns to output diverse samples that are similar but not identical to the training data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8512470 | EMBC |
Field | DocType | Volume |
Computer vision,Discriminator,Task analysis,Wearable computer,Computer science,Mobile device,Personally identifiable information,Artificial intelligence,Deep learning,Classifier (linguistics),Machine learning,Test data generation | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Skyler Norgaard | 1 | 0 | 0.68 |
| Ramyar Saeedi | 2 | 81 | 8.00 |
| Keyvan Sasani | 3 | 1 | 2.09 |
| Assefaw Hadish Gebremedhin | 4 | 218 | 28.60 |