Training of deep bidirectional RNNS for hand motion filtering via multimodal data fusion

Pathological Hand Tremor (PHT) is one of the most prevalent symptoms of some neurological movement disorders such as Parkinson's Disease (PD) and Essential Tremor (ET). Characterization, estimation, and extraction of PHT is a crucial requirement for assistive and robotic rehabilitation technologies that aim to counteract or resist PHT as an input noise to the system. In general, research in the literature on the topic of PHT removal can be categorized into two major categories, namely, classic and data-driven methods. Classic techniques use hand-crafted features and statistical processing pipelines to model and then extract the tremor while data-driven approaches are trained based on a sizable dataset to allow a computational model (such as neural networks) learn how to estimate the PHT. Since the availability of large datasets, especially in PHT estimation field is a bottleneck, in this feasibility study, we investigate the possibility of combining different recording modalities of PHT to generate a neural network for this purpose. This work explores the potential of jointly using accelerometer data and gyroscope recordings to produce a larger dataset for training a relatively complex network, which can potentially be extended for a deeper generalization.