Breakthrough Recognition in Robotic-Assisted UBE Surgery Based on Force Sensing and VAE-MLP

Background: Robotic-assisted unilateral biportal endoscopic surgery (UBE) is a more accurate and safer technique than traditional open surgical operations. The penetration recognition of ultrasonic drilling remains one of the challenging techniques of robotic-assisted UBE surgery. Methods: We propose a force and VAE-MLP-based method for real-time penetration recognition. During the ultrasonic drilling procedure, the force signals are collected and denoised via Kalman filtering first. The pre-processed data are then used to extract hidden features and perform classification by Variational Autoencoder (VAE) and Multilayer Perceptron (MLP), respectively, ultimately achieving real-time penetration recognition. Results: Our method achieves superior accuracy (99.32% vs. 95.90%) and faster inference speed (17 vs. 33 ms) compared to the classic time-series classification algorithm. Robotic ex vivo bone experiments further validated its efficacy. Conclusion: The force and VAE-MLP framework enables fast and accurate penetration detection, which offers a reliable and efficient solution for minimizing nerve damage in UBE surgery.