Journal
SENSORS
Volume 23, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/s23198158
Keywords
Parkinson's Disease; freezing of gait (FoG); GAN; DNN; artificial intelligence (AI)
Ask authors/readers for more resources
Data scarcity is a major challenge for artificial intelligence in healthcare. This study proposes a data augmentation solution using Generative Adversarial Networks (GANs) to generate synthetic data that is almost identical to the original data. The experiment conducted on a freezing of gait (FoG) symptom dataset demonstrates the effectiveness of this solution in alleviating the data shortage issue.
Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson's Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier's performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available