## 4 Discussion  
This study presents an important aspect and procedure for a robotic system aimed for use in electrode array insertion in cochlear implantation to avoid trauma. A novel system is developed for online prediction and correction of trajectory of the electrode array during electrode array insertion. This presents an application of an ensemble of machine learning algorithms for real-time closed-loop feedback control of an in vitro insertion process of an electrode array in cochlear implantation. It is the first step in this domain to develop a feedback controller for a robotic surgical procedure that can learn from its past experience and acquire the relationship between operating conditions and optimum actions. Data-driven machine learning models are trained and tested for trajectory (medial, lateral, and middle) and path (straight or curved) predictions during the insertion process. Overall, the shallow neural network achieved the highest performance in terms of accuracy. However, in order to improve performance, an ensemble learning approach was adopted with majority voting scheme. The ensemble learning model was developed with three machine learning algorithms, namely, shallow neural network, k-nearest neighbours, and random forest. Finally, our ensemble method achieved >80% accuracy for both offline and online trajectory and position/path classification. It has been demonstrated successfully in this work that complex impedance has merit as a sensing modality for atraumatic steering of an electrode array. However, other sensing mechanisms introduced in this domain, such as force sensing [43; 44], electroochelography (EcochG) [45], or facial nerve stimulation measurement unit [46], can be integrated into a  
Figure 9: Real-time correction of insertion trajectory. (**A**) Experimental setup with medial trajectory insertion, (**B**) experimental setup with correction to middle trajectory, (**C**) close-up of medial trajectory insertion, (**D**) correction of medial trajectory, (**E**) lateral trajectory, (**F**) correction of lateral trajectory. single unit along with complex impedance. Data generated by these sensing systems would carry more information about the EA insertion in real time than a single sensing system. This is termed data fusion, where data from different sources (sensors) are combined to reduce uncertainties, improve reliability, and increase the performance of the machine learning models [47]. It would also be interesting to correlate complex impedance with the insertion forces and train the ML models to stop the insertion when a certain threshold is exceeded to avoid intraoperative damage. As we have used classical machine learning algorithms for our trajectory prediction, there is a need to collect more data and test deep learning models that are believed to have more generalization ability and high performance. However, these deep learning models are data-hungry and they tend to overfit with few data examples. To record large datasets, we have to look at the reliability of the electrode array as well, since it is not made for the purpose of inserting hundred and thousands of times to collect large amounts of data. We may overcome this problem either with the availability of a decent amount of electrode arrays or we can apply data augmentation techniques to generate synthetic data [48]. We have proposed a solution for the trajectory and path/position prediction and correction of an electrode array using bipolar complex impedance measurements. This system may be helpful in predicting insertion failures such as irq foldover (this problem is more pronounced in perimodiolar EAs), EA bucking, and EA translocation. Further experiments need to be performed to demonstrate its applicability to overcome such failures that not only degrade CI postoperative performance but could also lead to internal infections. This system is open to further development and can be beneficial for automatic steering of an EA during insertion.