A mosquito proboscis-inspired cambered microneedle patch for ophthalmic regional anaesthesia

INTRODUCTION

One of the methods for pain management involves the use of local anesthesia, which numbs sensations in specific body regions while maintaining consciousness.

OBJECTIVES

Considering the certain limitations (e.g., pain, the requirement of skilled professionals, or slow passive diffusion) of conventional delivery methods of local anesthetics, developing alternative strategies that offer minimally invasive yet therapeutically effective delivery systems is of great concern for ophthalmic regional anesthesia.

METHODS AND RESULTS

In this study, a rapidly dissolving cambered microneedle (MNs) patch, composed of poly(vinylpyrrolidone) (PVP) and hyaluronic acid (HA) and served as a delivery system for lidocaine (Lido) in local anesthesia, was developed taking inspiration from the mosquito proboscis's ability to extract blood unnoticed. The lidocaine-containing MNs patch (MNs@Lido) consisted of 25 microneedles with a four-pronged cone structure (height: 500 μm, base width: 275 μm), arranged in a concentric circle pattern on the patch, and displays excellent dissolubility for effective drug delivery of Lido. After confirming good cytocompatibility, MNs@Lido was found to possess adequate rigidity to penetrate the cornea without causing any subsequent injury, and the created corneal pinhole channels completely self-healed within 24 h. Interestingly, MNs@Lido exhibited effective analgesic effects for local anesthesia on both heel skin and eyeball, with the sustained anesthetic effect lasting for at least 30 min.

CONCLUSIONS

These findings indicate that the mosquito proboscis-inspired cambered MNs patch provides rapid and painless local anesthesia, overcoming the limitations of conventional delivery methods of local anesthetics, thus opening up new possibilities in the treatment of ophthalmic diseases.

Head motion-corrected eye gaze tracking with the da Vinci surgical system

PURPOSE

To facilitate the integration of point of gaze (POG) as an input modality for robot-assisted surgery, we introduce a robust head movement compensation gaze tracking system for the da Vinci Surgical System. Previous surgical eye gaze trackers require multiple recalibrations and suffer from accuracy loss when users move from the calibrated position. We investigate whether eye corner detection can reduce gaze estimation error in a robotic surgery context.

METHODS

A polynomial regressor is first used to estimate POG after an 8-point calibration, and then, using another regressor, the POG error from head movement is estimated from the shift in 2D eye corner location. Eye corners are computed by first detecting regions of interest using the You Only Look Once (YOLO) object detector trained on 1600 annotated eye images (open dataset included). Contours are then extracted from the bounding boxes and a derivative-based curvature detector refines the eye corner.

RESULTS

Through a user study (n = 24), our corner-contingent head compensation algorithm showed an error reduction in degrees of visual angle of 1.20∘ (p = 0.037) for the left eye and 1.26∘ (p = 0.079) for the right compared to the previous gold-standard POG error correction method. In addition, the eye corner pipeline showed a root-mean-squared error of 3.57 (SD = 1.92) pixels in detecting eye corners over 201 annotated frames.

CONCLUSION

We introduce an effective method of using eye corners to correct for eye gaze estimation, enabling the practical acquisition of POG in robotic surgery.

Application of Medical Image Navigation Technology in Minimally Invasive Puncture Robot

Microneedle puncture is a standard minimally invasive treatment and surgical method, which is widely used in extracting blood, tissues, and their secretions for pathological examination, needle-puncture-directed drug therapy, local anaesthesia, microwave ablation needle therapy, radiotherapy, and other procedures. The use of robots for microneedle puncture has become a worldwide research hotspot, and medical imaging navigation technology plays an essential role in preoperative robotic puncture path planning, intraoperative assisted puncture, and surgical efficacy detection. This paper introduces medical imaging technology and minimally invasive puncture robots, reviews the current status of research on the application of medical imaging navigation technology in minimally invasive puncture robots, and points out its future development trends and challenges.

A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model

Rapid and accurate prediction of interaction force is an effective way to enhance the compliant control performance. However, whether individual muscles or a combination of muscles is more suitable for interaction force prediction under different contraction tasks is of great importance in the compliant control of the wearable assisted robot. In this article, a novel algorithm that is based on sEMG and KPCA-DRSN is proposed to explore the relationship between interaction force prediction and sEMG signals. Furthermore, the contribution of each muscle to the interaction force is assessed based on the predicted results. First of all, the experimental platform for obtaining the sEMG is described. Then, the raw sEMG signal of different muscles is collected from the upper arm during different contractions. Meanwhile, the output force is collected by the force sensor. The Kernel Principal Component Analysis (KPCA) method is adopted to remove the invalid components of the raw sEMG signal. After that, the processed sequence is fed into the Deep Residual Shrinkage Network (DRSN) to predict the interaction force. Finally, based on the prediction results, the contribution of each sEMG signal from different muscles to the interaction force is evaluated by the mean impact value (MIV) indicator. The experimental results demonstrate that our methods can automatically extract the valid features of sEMG signal and provided fast and efficient prediction. In addition, the single muscle with the largest MIV index could predict the interaction force faster and more accurately than the muscle combination in different contraction tasks. The finding of our research provides a solid evidence base for the compliant control of the wearable robot.

Improving Haptic Response for Contextual Human Robot Interaction

For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One of the solutions to improve the device response is to infer human intended motion and move the robot at the earliest time possible to the desired goal. This paper presents an experimental study to improve the prediction time and reduce the robot time taken to reach the desired position. We developed motion strategies based on the hand motion and eye-gaze direction to determine the point of user interaction in a virtual environment. To assess the performance of the strategies, we conducted a subject-based experiment using an exergame for reach and grab tasks designed for upper limb rehabilitation training. The experimental results in this study revealed that eye-gaze-based prediction significantly improved the detection time by 37% and the robot time taken to reach the target by 27%. Further analysis provided more insight on the effect of the eye-gaze window and the hand threshold on the device response for the experimental task.

A New Feature Extraction and Recognition Method for Microexpression Based on Local Non-negative Matrix Factorization

Microexpression is usually characterized by short duration and small action range, and the existing general expression recognition algorithms do not work well for microexpression. As a feature extraction method, non-negative matrix factorization can decompose the original data into different components, which has been successfully applied to facial recognition. In this paper, local non-negative matrix factorization is explored to decompose microexpression into some facial muscle actions, and extract features for recognition based on apex frame. However, the existing microexpression datasets fall short of samples to train a classifier with good generalization. The macro-to-micro algorithm based on singular value decomposition can augment the number of microexpressions, but it cannot meet non-negative properties of feature vectors. To address these problems, we propose an improved macro-to-micro algorithm to augment microexpression samples by manipulating the macroexpression data based on local non-negative matrix factorization. Finally, several experiments are conducted to verify the effectiveness of the proposed scheme, which results show that it has a higher recognition accuracy for microexpression compared with the related algorithms based on CK+/CASME2/SAMM datasets.