Experimental evaluation of accuracy and efficiency of two control strategies for a novel foot commanded robotic laparoscope holders with surgeons.

The implementation of a laparoscope-holding robot in minimally invasive surgery enhances the efficiency and safety of the operation. However, the extra robot control task can increase the cognitive load on surgeons. A suitable interface may simplify the control task and reduce the surgeon load. Foot interfaces are commonly used for commanding laparoscope-holding robots, with two control strategies available: decoupled control permits only one Cartesian axis actuation, known as decoupled commands; hybrid control allows for both decoupled commands and multiple axes actuation, known as coupled commands. This paper aims to determine the optimal control strategy for foot interfaces by investigating two common assumptions in the literature: (1) Decoupled control is believed to result in better predictability of the final laparoscopic view orientation, and (2) Hybrid control has the efficiency advantage in laparoscope control. Our user study with 11 experienced and trainee surgeons shows that decoupled control has better predictability than hybrid control, while both approaches are equally efficient. In addition, using two surgery-like tasks in a simulator, users' choice of decoupled and coupled commands is analysed based on their level of surgical experience and the nature of the movement. Results show that trainee surgeons tend to issue more commands than the more experienced participants. Single decoupled commands were frequently used in small view adjustments, while a mixture of coupled and decoupled commands was preferred in larger view adjustments. A guideline for foot interface control strategy selection is provided.

Effect of Tibial Component Misalignment on the Lower Limb Joint Kinematics During Squat.

The component orientation of the total knee replacement is critical to surgical outcomes. There have been many studies focused on knee movement for different component rotations. However, the effect of component misalignment on a dynamic movement, especially which requires high knee flexion, is not widely studied. The aim of this study is to investigate the effect of tibial component misalignment on a squat motion by predictive simulation. Squat motions with different replacement component alignments were predicted by formulating an optimal control problem. The result indicates that component misalignment on coronal and horizontal planes reduces peak joint flexion angles and the external rotation on the horizontal plane has the most negative impact. Misalignment in external rotation resulted in the greatest reduction of peak joint flexion angles. The simulation was validated by comparison with experimental data, which showed a high level of correlation with the predicted motion.Clinical relevance- The predictive simulation presented in this study can predict the dynamic post-surgery movement of TKR. It has the potential to help surgeons and clinicians at the preoperative planning stage.

A Novel Perception Framework for Automatic Laparoscope Zoom Factor Control Using Tool Geometry.

In conventional Minimally Invasive Surgery, the surgeon conducts the operation while a human or robot holds the laparoscope. Laparoscope control is returned to the surgeon in teleoperated camera holding robots, but simultaneously controlling the laparoscope and surgical tools might be cognitively demanding. On the other hand, fully automated camera holders are still limited in their performance. To help the surgeon to better focus on the main operation while maintaining their control authority, we propose an automatic laparoscope zoom factor control framework for Robot-Assisted Minimally Invasive Surgery. In this paper, we present the perception section of the framework. It extracts and uses the surgical tool's geometric characteristics to adjust the laparoscope's zoom factor, without any artificial markers. The acceptable range and tooltip's position frequency during operations are analysed based on the gallbladder removal surgery dataset (Cholec80). The common range and tooltip's heatmap are identified and presented quantitatively.

Classification of squat quality with inertial measurement units in the single leg squat mobility test.

Many assessment and diagnosis protocols in rehabilitation, orthopedic surgery and sports medicine rely on mobility tests like the Single Leg Squat (SLS). In this study, a set of three Inertial Measurement Units (IMUs) were used to estimate the joint pose during SLS and to classify the SLS as poor, moderate or good. An Extended Kalman Filter pose estimation method was used to estimate kinematic joint variables, and time domain features were generated based on these variables. The most important features were then selected and used to train Support Vector Machine (SVM), Linear Multinomial Logistic Regression, and Decision Tree classifiers. The results of feature selection highlight the importance of the ankle internal rotation (IR) angle in classifying SLS. Classification results on a human motion dataset achieved an accuracy of 98% for the two-class problem using SVM, while for 3 class classification, the maximum accuracy was 73% using Decision Tree.

Human pose recovery for rehabilitation using ambulatory sensors.

In this paper, an approach for lower-leg pose recovery from ambulatory sensors is implemented and validated in a clinical setting. Inertial measurement units are attached to patients undergoing physiotherapy. The sensor data is combined with a kinematic model within an extended Kalman filter framework to perform joint angle estimation. Anthropometric joint limits and process noise adaptation are employed to improve the quality of the joint angle estimation. The proposed approach is tested on 7 patients following total hip or knee joint replacement surgery. The proposed approach achieves an average root-mean-square error of 0.12 radians at key poses.