Prediction of Voluntary Motion Using Decomposition-and-Ensemble Framework with Deep Neural Networks

It is essential for seamlessly delivering intended hand motion to surgical robots while actively suppressing undesired hand tremor during microsurgery. To achieve this goal, we propose a novel method for predicting voluntary motion based on deep learning with the signal decomposition and ensemble approach. This approach can thus deal with various forms of voluntary signals, such as either highly stationary or rather highly cyclic at a low range of frequencies. The proposed method comprises a series of signal blocks to decompose complex hand motion into multiple sub-signals using deep neural networks upon their signal characteristics. The signal block yields parameterized sub-signal and predicted voluntary motion. In addition, an ensemble layer allows for accurately predicting future voluntary motion by combining predicted motion from each signal block with the optimal weight. These signal blocks are connected by a decompositionfiow in series and also by a forecastfiow in parallel to ensemble the prediction output of each block. Given real data sets, we evaluated the prediction performance of the proposed algorithm compared to other data-driven deep learning models. The generalizability of the proposed algorithm was also investigated by applying the trained models to new data sets from new tasks and a different subject, which had not been involved in training procedures. As a result, the proposed algorithm outperforms the other baseline models in terms of prediction error and accuracy. Furthermore, we explored whether the proposed method could suppress tremor via spectral analysis, which shows substantial tremor attenuation more than-10 dB in a frequency of interest, 6-14 Hz. It is also found that the proposed method has the predictive power for dealing with inevitable control delays, where prediction error increased only by 2% per one-time sample, approximately 4.2 ms.