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To develop advanced rehabilitation and assistive robotic technologies for patients wit pathological hand tremor (PHT), accurate estimation and prediction of nonstationary PHT is critical. However, the required level of accuracy and latency has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This work addresses this unmet need through establishing a deep recurrent neural network model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.
