Simulating Accelerometer Signals of Parkinson's Gait Using Generative Adversarial Networks

Wearable technologies have been demonstrated to have value in the objective assessment of Parkinson's disease. However, certain symptoms such as freezing of gait are challenging to monitor using current approaches. Data augmentation, wherein synthetic or simulated data is added to real world training sets to increase their size and diversity, has emerged as an approach to bolster the accuracy of machine learning models in other healthcare domains such as medical imaging. In this paper, we evaluate the use of generative adversarial networks to create synthetic accelerometry data representing both typical and freezing gait patterns. Our preliminary results suggest that these synthetic datasets can mimic realistic movement patterns of individuals with Parkinson's disease. In the future, we will assess the impact that integrating these synthetic data into training sets has on the accuracy of freezing of gait detection algorithms.