Tremor estimation and filtering in robotic-assisted surgery

In surgical procedures, surgeons can suffer from spontaneous hand tremors that can affect the accuracy of surgical robots. Therefore, it is necessary to measure and model the tremor signal by sensors to suppress hand tremor. This paper proposes a prediction method based on deep learning that integrates long-term and short-term features to achieve this goal. The long-term features of tremor signals are extracted using a bidirectional Long-short-term memory network, while the short-term features are extracted using a Temporal Convolutional Network. By integrating the long-term and short-term characteristics of tremor signals, this approach provides rich temporal information for signal estimation. In addition, genetic algorithm is used to obtain the optimal time step-size to fully explore the temporal correlation of signals, and an end data compensation strategy is adopted to ensure that the tremor filtering covers the entire process. The performance of the proposed method is evaluated by training and testing on the same dataset as other methods, and conducting suture experiments in a virtual surgical environment. The results show that our proposed model is superior to the existing methods, effectively reducing the tremor signals estimation error. This method can provide better tremor estimation and compensation performance, effectively suppressing the hand tremors and improving the surgical accuracy.