The effect of static stretching of agonist and antagonist muscles on knee joint position sense

Background/Aims Proprioception acuity is important in sports activities and stretching is widely used in warm-up programmes. The main objective of this study was to evaluate if a warm-up programme with and without stretching flexors and extensors muscles could affect knee joint position sense. Methods The effects of different stretching regimens on joint position sense were examined. A total of 12 semi-professional football players completed four warm-up sessions over 4 weeks: standard warm-up programme; standard warm-up programme with quadriceps stretching; standard warm-up programme with hamstring stretching; and standard warm-up programme with stretching of both quadriceps and hamstrings. Open kinetic chain knee joint position sense was estimated from the ability to reproduce the three target angles (20°, 45° and 60° knee flexion) in the dominant limb before and after the intervention. Results In the absolute angular error, there was a statistically significant three-way interaction between the warm-up programme, target angle and time (F (6, 54)=6.88, P=0.001). Findings of post-hoc analysis demonstrated that there was a statistically significant difference between the pre- and post-stretching of hamstrings for the target angles of 20° (4.70 vs 1.57, P=0.01), 45° (1.70 vs 4.50, P=0.02), and 60° (1.93 vs 4.20, P=0.02). In the relative angular error, interaction of time by the warm-up programme was significant (F (3, 27)=3.41, P=0.03). Conclusions The warm-up programme with static stretching of hamstrings had a negative effect on open kinetic chain knee joint position sense during the flexion to extension repositioning task, which may not only have a negative effect on performance of athletes as a part of warm-up exercises, but may also lead to further injuries.

Accuracy of Electrode Placement in IRE Treatment with Navigated Guidance

Purpose Evaluate the accuracy of multiple electrode placements in IRE treatment of liver tumours using a stereotactic CT-based navigation system. Method Analysing data from all IRE treatments of liver tumours at one institution until 31 December 2018. Comparing planned with validated electrode placement. Analysing lateral and angular errors and parallelism between electrode pairs Results Eighty-four tumours were treated in 60 patients. Forty-six per cent were hepatocellular carcinoma, and 36% were colorectal liver metastases. The tumours were located in all segments of the liver. Data were complete from 51 treatments. Two hundred and six electrodes and 336 electrode pairs were analysed. The median lateral and angular error, comparing planned and validated electrode placement, was 3.6 mm (range 0.2–13.6 mm) and 3.1° (range 0°–16.1°). All electrodes with a lateral error >10 mm were either re-positioned or excluded before treatment. The median angle between the electrode pairs was 3.8° (range 0.3°–17.2°). There were no electrode placement-related complications. Conclusion The use of a stereotactic CT-based system for navigation of electrode placement in IRE treatment of liver tumours is safe, accurate and user friendly.

Visual Robot Relocalization Based on Multi-Task CNN and Image-Similarity Strategy

The traditional CNN for 6D robot relocalization which outputs pose estimations does not interpret whether the model is making sensible predictions or just guessing at random. We found that convnet representations trained on classification problems generalize well to other tasks. Thus, we propose a multi-task CNN for robot relocalization, which can simultaneously perform pose regression and scene recognition. Scene recognition determines whether the input image belongs to the current scene in which the robot is located, not only reducing the error of relocalization but also making us understand with what confidence we can trust the prediction. Meanwhile, we found that when there is a large visual difference between testing images and training images, the pose precision becomes low. Based on this, we present the dual-level image-similarity strategy (DLISS), which consists of two levels: initial level and iteration-level. The initial level performs feature vector clustering in the training set and feature vector acquisition in testing images. The iteration level, namely, the PSO-based image-block selection algorithm, can select the testing images which are the most similar to training images based on the initial level, enabling us to gain higher pose accuracy in testing set. Our method considers both the accuracy and the robustness of relocalization, and it can operate indoors and outdoors in real time, taking at most 27 ms per frame to compute. Finally, we used the Microsoft 7Scenes dataset and the Cambridge Landmarks dataset to evaluate our method. It can obtain approximately 0.33 m and 7.51∘ accuracy on 7Scenes dataset, and get approximately 1.44 m and 4.83∘ accuracy on the Cambridge Landmarks dataset. Compared with PoseNet, our CNN reduced the average positional error by 25% and the average angular error by 27.79% on 7Scenes dataset, and reduced the average positional error by 40% and the average angular error by 28.55% on the Cambridge Landmarks dataset. We show that our multi-task CNN can localize from high-level features and is robust to images which are not in the current scene. Furthermore, we show that our multi-task CNN gets higher accuracy of relocalization by using testing images obtained by DLISS.

Accuracy analysis and optimization of an extendible support structure with joint clearances

An extendible support structure (ESS) used for unfolding and supporting the antenna array of the Synthetic Aperture Radar (SAR) satellite is reviewed and modeled in this paper. The structure is parameterized by calibrating 12 independent parameters, and following which, angular accuracy of the ESS with joint clearances is modeled. The maximum angular error is obtained by the particle swarm optimization (PSO) and validated by the Monte Carlo simulation. A novel error reduction method is then proposed to improve the accuracy of the structure. In the proposed method, the uncertainty of the joint clearance is eliminated using force constraints by adding small torsional springs. Various joint clearance models with force constraints are proposed to obtain the optimal spring allocation, and based on which, the angular error is further reduced by optimizing the structure of the ESS. The Quasi-Monte-Carlo-based Sobol method for global sensitivity analysis is used to select the design parameters for optimization. Finally, the angular error is greatly reduced.

Two Hundred Twenty-Six Consecutive Deep Brain Stimulation Electrodes Placed Using an “Asleep” Technique and the Neuro|MateTM Robot for the Treatment of Movement Disorders

BACKGROUND Robotics in neurosurgery has demonstrated widening indications and rapid growth in recent years. Robotic precision and reproducibility are especially pertinent to the field of functional neurosurgery. Deep brain stimulation (DBS) requires accurate placement of electrodes in order to maximize efficacy and minimize side effects. In addition, asleep techniques demand clear target visualization and immediate on-table verification of accuracy. OBJECTIVE To describe the surgical technique of asleep DBS surgery using the Neuro|MateTM Robot (Renishaw plc, Wotton-under-Edge, United Kingdom) and examine the accuracy of DBS lead placement in the subthalamic nucleus (STN) for the treatment of movement disorders. METHODS A single-center retrospective review of 113 patients who underwent bilateral STN/Zona Incerta electrode placement was performed. Accuracy of implantation was assessed using 5 measurements, Euclidian distance, radial error, depth error, angular error, and shift error. RESULTS A total of 226 planned vs actual electrode placements were analyzed. The mean 3-dimensional vector error calculated for 226 trajectories was 0.78 +/− 0.37 mm. The mean radial displacement off planned trajectory was 0.6 +/− 0.33 mm. The mean depth error, angular error, and shift error was 0.4 +/− 0.35 mm, 0.4 degrees, and 0.3 mm, respectively. CONCLUSION This report details our institution's method for DBS lead placement in patients under general anaesthesia using anatomical targeting without microelectrode recordings or intraoperative test stimulation for the treatment of movement disorders. This is the largest reported dataset of accuracy results in DBS surgery performed asleep. This novel robot-assisted operative technique results in sub-millimeter accuracy in DBS electrode placement.