Patient-to-medical image registration is a crucial factor that affects the accuracy of image-guided ENT- and neurosurgery systems. In this study, a novel registration protocol that extracts the point cloud in the patient space using the contact approach was proposed. To extract the optimal point cloud in patient space, we propose a multi-step registration protocol consisting of augmentation of the point cloud and creation of an optimal point cloud in patient space that satisfies the minimum distance from the point cloud in the medical image space. A hemisphere mathematical model and plastic facial phantom were used to validate the proposed registration protocol. An optical and electromagnetic tracking system, of the type that is commonly used in clinical practice, was used to acquire the point cloud in the patient space and evaluate the accuracy of the proposed registration protocol. The SRE and TRE of the proposed protocol were improved by about 30% and 50%, respectively, compared to those of a conventional registration protocol. In addition, TRE was reduced to about 28% and 21% in the optical and electromagnetic methods, respectively, thus showing improved accuracy. The new algorithm proposed in this study is expected to be applied to surgical navigation systems in the near future, which could increase the success rate of otolaryngological and neurological surgery.

Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an insole sensor was proposed to replace the prior methods that have spatial constraints. The protocol is hierarchically composed of a classification model and a regression model to predict joint moments. Additionally, a single LSTM model was developed to compare with proposed protocol. To optimize hyperparameters of the machine learning model and input feature, Bayesian optimization method was adopted. As a result, the proposed protocol showed a relative root mean square error (rRMSE) of 8.06~13.88% while the single LSTM showed 9.30~18.66% rRMSE. This protocol in this research is expected to be a starting point for developing a system for estimating the lower extremity and L5/S1 moment during lifting that can replace the complex prior method and adopted to workplace environments. This novel study has the potential to precisely design a feedback iterative control system of an exoskeleton for the appropriate generation of an actuator torque.