CAM-FoC: A High Accuracy Lightweight Deep Neural Network for Grip Force Measurement of Elongated Surgical Instrument

Abstract

Grip force measurement enables better control performance for robot-assisted minimally invasive surgery (RMIS). A deep-learning-based method is proposed to measure the instrument grip force without mounting additional sensors in this article. First, the training trajectory and input data frame are studied. Seven data are derived from the original sensor data, and a binary butterfly optimization algorithm with opposite-based learning (bBOA-OBL) is conducted to form the suitable input data frame. Based on the data frame, a novel convolutional network with an attention mechanism and feedforward of current (CAM-FoC) is proposed to calculate the grip force. The results of the ablation study tell that the master-slave trajectory has the highest accuracy, the optimized input data frame can reduce the error by 17%, and each component of CAM-FoC can enhance the measurement accuracy. Experiments and comparisons are also carried out. The root mean squared error (RMSE) in the experiment is only 0.1233 N and is lower than four other popular methods. In addition, the average computation time is around 2 ms on different platforms. The method manifests higher measurement accuracy than the state-of-the-art and is of acceptable computational complexity. The technology would potentially achieve grip force feedback in clinical usage, which can significantly improve surgery performance. The method could also be used by other robots composed of the cable-pulley mechanism to acquire external forces.