RESUMEN

BACKGROUND: In ultrasound-guided minimally invasive surgery (MIS) of tumours, it is crucial to discover the optimal scanning plane (OSP) and organise the MIS scalpel work trajectory in this plane. The OSP can be altered and is challenging to track when the scalpel interacts with deformed tissues. Therefore, tracking the OSP becomes critical in MIS. In master-slave control, virtual force (VF) is used to assist the operator in completing the task. However, most literature assumes that the environment is sufficiently stable. No specific method focuses on tracking the OSP of the lesion within largely deformed tissues. METHODS: This paper used the improved artificial potential field method to establish the VF that could guide the operator to track the OSP. When tissue deformation occurred, an artificial neural network (ANN) was used to predict the target position, guiding the operator to find the new OSP. An experimental robot platform was built to verify the proposed algorithm's effects. Experiments to track the OSP were performed on a phantom. RESULTS: The results showed that the presented method could reduce the trajectory redundancy of ultrasonic scanning, shorten the time of OSP discovery and tracking, and decrease the deviation between the ultrasonic scanning position and the OSP. CONCLUSIONS: This method has significance for the accurate localization and successful removal of tumours. Future work will focus on improving the adaptability of the proposed ANN prediction model in different phantoms.

Asunto(s)

Fenómenos Mecánicos , Neoplasias , Humanos , Ultrasonografía , Fantasmas de Imagen , Procedimientos Quirúrgicos Mínimamente Invasivos/métodos

RESUMEN

In the process of rehabilitation, the objectivity and the accuracy of rehabilitation assessment have an obvious impact on the follow-up training. To improve this problem, using a multi-sensor source, this paper attempts to establish a comprehensive assessment method of the finger rehabilitation effect, including three indicators of finger muscle strength, muscle fatigue degree, and range of motion. Firstly, on the basis of the fingertip pressure sensor of the End-Effector Finger Rehabilitation Robot, a mathematical model of finger muscle strength estimation was established, and the estimated muscle strength was scored using the entropy weight method. Secondly, using an sEMG signal sensor, a fatigue monitoring system was designed in the training process, and the fatigue degree was determined on the basis of the change trend of the eigenvalues of MAV and RMS. Lastly, a human-machine motion coupling model was established, and the joint range of motion acquisition and scoring model were obtained on the basis of the motor encoder. According to the above three indicators, using the AHP assessment method to establish a comprehensive rehabilitation assessment method, the effectiveness of the method was verified by experiments. This paper provides a potential new idea and method for objective, accurate, and convenient assessment of finger function rehabilitation, which is of positive significance for alleviating the burden on rehabilitation doctors and improving rehabilitation efficiency.