Physiological studies in humans proved that the central nervous system automatically integrates the information from different sensory systems. Inspired by the integration mechanism, this study aims to yield accurate perception of the surgical field during milling operation. The dynamic model of the surgical milling process is developed theoretically, and the model considers the influence of the unbalance in the rotating machine and the time-variant cutting force. On the basis of the theoretical analysis, the monitoring of the milling process can be realized to determine the state of the operation. An accelerometer and a microphone are used to measure the acceleration of the milling device and the sound pressure generated during bone milling separately, and then the correlation coefficient between the harmonics in the two signals is fed into the artificial neural network. The output of the artificial neural network is used to determine whether the milling device is cutting the cortical bone, the cancellous bone, the muscle or nothing (i.e., idle running). The experimental results in in vitro porcine spines show that the proposed method is very sensitive to the phenomenon of tissue wrapping in the surgical field, and it can identify the predetermined states correctly.
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Dai et al. (2018) studied this question.
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