Multisensory Integration for Identifying the Milling States in Robot-Assisted Cervical Laminectomy

In spinal surgery, precise identification of high-speed bur milling states is crucial for patient safety. This study investigates whether integrating tactile and auditory perception can enhance the accuracy of milling state detection in robot-assisted cervical laminectomy.

Based on the mathematical and physical model of vibration and sound in high-speed bur milling bone, the feasibility of employing vibration and sound characteristics to identify the milling states of high-speed bur is studied systematically. Cervical laminectomy was performed on the cervical spine of the sheep. During the signal acquisition process, acceleration sensors and microphones were installed to collect vibration and sound signals, respectively. Seven milling states were set up in the experiment: (1) Milling depths of cortical bone (CTB): 0.5, 1.0, and 1.5 mm; (2) Milling depths of milling of cancellous bone (CCB): 0.5, 1.0, and 1.5 mm; (3) Boundary conditions: high-speed bur idling or complete penetration of bone (PT). The milling speed was set at 0.5 mm/s, the milling angle was 45°, and the bur diameter was 4 mm. The vibration or sound was extracted by Fast Fourier Transform (FFT) in the frequency domain of the first nine harmonics to generate the feature vector in 9 dimensions (9-D) space. These vibration and sound features were combined to form an 18-D multi-perception spatial vector for subsequent analysis, including five machine learning algorithms: Support Vector Machine (SVM), K Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Decision Tree (DT), and deep learning models: Long Short-Term Memory networks (LSTM).

Based on the 18-D features of tactile and auditory multisensory fusion, the LSTM model is trained using 6600 sets of high-speed bur milling data. In order to achieve the best performance, a layer-by-layer parameter optimization strategy was used to determine the optimal parameter configuration, and finally, a single-layer LSTM with 12 memory units was constructed. In terms of accuracy and stability, the model is significantly superior to the machine learning algorithms (SVM, KNN, NB, LDA, and DT), and the accuracy of LSTM is 99.32% in the milling states identification of cervical lamina milling with high-speed bur.

Through theoretical analysis and experimental verification, the study built a multi-perception fusion framework based on tactile and auditory perception and accurately identified the cervical vertebra milling states through the LSTM model, which can provide perception means for operational spinal surgery robots in the future.