A fast, accurate and uncalibrated robotic puncture method.

BACKGROUND: Robotic puncture system (RPS) consists of an optical tracking system (OTS) and a robotic arm gripping the puncture needle. Typically, the RPS requires hand-eye calibration before the surgery in order to obtain the relative position between the OTS and the robotic arm. However, if there is any displacement or angular deviation in either the robotic arm or the OTS, the calibration results become invalid, necessitating recalibration. METHODS: We propose an uncalibrated robotic puncture method that does not rely on the hand-eye relationship of the RPS. By constructing angle and position graph jacobian matrices respectively, and employing Square Root Cubature Kalman Filter for online estimation. This enables obtaining control variables for the robot to perform puncture operations. RESULTS: In simulation experiments, our method achieves an average error of 1.3495 mm and an average time consumption of 39.331 s. CONCLUSIONS: Experimental results indicate that our method possesses high accuracy, low time consumption, and strong robustness.

[Uncalibrated Positioning Method for Puncture Robots Based on Optical Navigation].

Robotic puncture system has been widely used in modern minimally invasive surgery, which usually uses hand-eye calibration to calculate the spatial relationship between the robot and the optical tracking system. However, the hand-eye calibration process is time-consuming and sensitive to environmental changes, which makes it difficult to guarantee the puncture accuracy of the robot. This study proposes an uncalibrated positioning method for puncture robot based on optical navigation. The method divides the target path positioning into two stages, angle positioning and position positioning, and designs angle image features and position image features respectively. The corresponding image Jacobian matrix is constructed based on the image features and updated by online estimation with a cubature Kalman filter to drive the robot to perform target path localization. The target path positioning results show that the method is more accurate than the traditional hand-eye calibration method and saves significant preoperative preparation time by eliminating the need for calibration.

Nystagmus patterns classification framework based on deep learning and optical flow.

Benign paroxysmal positional vertigo (BPPV) is the most common vestibular peripheral vertigo disease characterized by brief recurrent vertigo with positional nystagmus. Clinically, it is common to recognize the patterns of nystagmus by analyzing infrared nystagmus videos of patients. However, the existing approaches cannot effectively recognize different patterns of nystagmus, especially the torsional nystagmus. To improve the performance of recognizing different nystagmus patterns, this paper contributes an automatic recognizing method of BPPV nystagmus patterns based on deep learning and optical flow to assist doctors in analyzing the types of BPPV. Firstly, we present an adaptive method for eliminating invalid frames that caused by eyelid occlusion or blinking in nystagmus videos and an adaptive method for segmenting the iris and pupil area from video frames quickly and efficiently. Then, we use a deep learning-based optical flow method to extract nystagmus information. Finally, we propose a nystagmus video classification network (NVCN) to categorize the patterns of nystagmus. We use ConvNeXt to extract eye movement features and then use LSTM to extract temporal features. Experiments conducted on the clinically collected datasets of infrared nystagmus videos show that the NVCN model achieves an accuracy of 94.91% and an F1 score of 93.70% on nystagmus patterns classification task as well as an accuracy of 97.75% and an F1 score of 97.48% on torsional nystagmus recognition task. The experimental results prove that the framework we propose can effectively recognize different patterns of nystagmus.

Geometric calibration of markerless optical surgical navigation system.

BACKGROUND: Patient-to-image registration is required for image-guided surgical navigation, but marker-based registration is time consuming and is subject to manual error. Markerless registration is an alternative solution to avoid these issues. METHODS: This study designs a calibration board and proposes a geometric calibration method to calibrate the near-infrared tracking and structured light components of the proposed optical surgical navigation system simultaneously. RESULTS: A planar board and a cylinder are used to evaluate the accuracy of calibration. The mean error for the board experiment is 0.035 mm, and the diameter error for the cylinder experiment is 0.119 mm. A calibration board is reconstructed to evaluate the accuracy of the calibration, and the measured mean error is 0.012 mm. A head phantom is reconstructed and tracked by the proposed optical surgical navigation system. The tracking error is less than 0.3 mm. CONCLUSIONS: Experimental results show that the proposed method obtains high accessibility and accuracy and satisfies application requirements.

Stroke-Related Changes in the Complexity of Muscle Activation during Obstacle Crossing Using Fuzzy Approximate Entropy Analysis.

This study investigated the complexity of the electromyography (EMG) of lower limb muscles when performing obstacle crossing tasks at different heights in poststroke subjects versus healthy controls. Five poststroke subjects and eight healthy controls were recruited to perform different obstacle crossing tasks at various heights (randomly set at 10, 20, and 30% of the leg's length). EMG signals were recorded from bilateral biceps femoris (BF), rectus femoris (RF), medial gastrocnemius, and tibialis anterior during obstacle crossing task. The fuzzy approximate entropy (fApEn) approach was used to analyze the complexity of the EMG signals. The fApEn values were significantly smaller in the RF of the trailing limb during the swing phase in poststroke subjects than healthy controls (p < 0.05), which may be an indication of smaller number and less frequent firing rates of the motor units. However, during the swing phase, there were non-significant increases in the fApEn values of BF and RF in the trailing limb of the stroke group compared with those of healthy controls, resulting in a coping strategy when facing challenging tasks. The fApEn values that increased with height were found in the BF of the leading limb during the stance phase and in the RF of the trailing limb during the swing phase (p < 0.05). The reason for this may have been a larger muscle activation associated with the increase in obstacle height. This study demonstrated a suitable and non-invasive method to evaluate muscle function after a stroke.