Traditional injection and extraction devices often appear painful and cumbersome for patients. In recent years, polymer microneedles (MNs) have become a novel tool in the field of clinical medicine and health. However, the cost of building MNs into any shapes still remains a challenge. In this paper, we proposed hydrogel microneedles fabricated by high-precision digital light processing (H-P DLP) 3D printing system. Benefits from the sharp protuberance and micro-porous of the hydrogel microneedle, the microneedle performed multifunctional tasks such as drug delivery and detection with minimally invasion. Critical parameters for the fabrication process were analyzed, and the mechanical properties of MNs were measured to find a balance between precision and stiffness. Results shows that the stiffness and precision were significantly influenced by exposure time of each layer, and optimized printing parameters provided a balance between precision and stiffness. Bio-compatible MNs based on our H-P DLP system was able to execute drug injection and drug detection in our experiments. This work provided a low-cost and fast method to build MNs with 3D building, qualified the mechanical performance, drug injection, drug detection ability of MNs, and may be helpful for the potential clinical application.

Gait is a basic characteristic of human motion. Different gaits are usually associated with different body functions. Gait recognition has wide applications in clinical medicine, rehabilitation training, posture recognition, and other fields. At present, many wearable systems based on MEMS pressure and inertial sensors have been developed and used for gait recognition. However, there has been a lack of a comprehensive summary and comparison of these systems from the perspectives of their hardware compositions, working principles, algorithm models, and applications. This review aims to promote the development of wearable gait recognition devices. First, sensor technologies for gait measurement are summarized from the perspectives of their working principles and characteristics. Then, these technologies are compared in terms of the performance of algorithms for data preprocessing, cycle and phase segmentation, and gait recognition. Next, the applications of MEMS sensor-based gait recognition systems in various fields are summarized. Finally, some limitations of existing wearable gait recognition systems and their future directions are discussed.

Cell microinjection is a technique of precise delivery of substances into cells and is widely used for studying cell transfection, signaling pathways, and organelle functions. Microinjection of the embryos of zebrafish, the third most important animal model, has become a very useful technique in bioscience. However, factors such as the small cell size, high cell deformation tendency, and transparent zebrafish embryo membrane make the microinjection process difficult. Furthermore, this process has strict, specific requirements, such as chorion softening, avoiding contacting the first polar body, and high-precision detection. Therefore, highly accurate control and detection platforms are critical for achieving the automated microinjection of zebrafish embryos. This article reviews the latest technologies and methods used in the automated microinjection of zebrafish embryos and provides a detailed description of the current developments and applications of robotic microinjection systems. The review covers key areas related to automated embryo injection, including cell searching and location, cell position and posture adjustment, microscopic visual servoing control, sensors, actuators, puncturing mechanisms, and microinjection.

Drone detection is a significant research topic due to the potential security threats posed by the misuse of drones in both civilian and military domains. However, traditional drone detection methods are challenged by the drastic scale changes and complex ambiguity during drone flight, and it is difficult to detect small target drones quickly and efficiently. We propose an information-enhanced model based on improved YOLOv5 (TGC-YOLOv5) for fast and accurate detection of small target drones in complex environments. The main contributions of this paper are as follows: First, the Transformer encoder module is incorporated into YOLOv5 to augment attention toward the regions of interest. Second, the Global Attention Mechanism (GAM) is embraced to mitigate information diffusion among distinct layers and amplify the global cross-dimensional interaction features. Finally, the Coordinate Attention Mechanism (CA) is incorporated into the bottleneck part of C3, enhancing the extraction capability of local information for small targets. To enhance and verify the robustness and generalization of the model, a small target drone dataset (SUAV-DATA) is constructed in all-weather, multi-scenario, and complex environments. The experimental results show that based on the SUAV-DATA dataset, the AP value of TGC-YOLOv5 reaches 0.848, which is 2.5% higher than the original YOLOv5, and the Recall value of TGC-YOLOv5 reaches 0.823, which is a 3.8% improvement over the original YOLOv5. The robustness of our proposed model is also verified on the Real-World open-source image dataset, achieving the best accuracy in light, fog, stain, and saturation pollution images. The findings and methods of this paper have important significance and value for improving the efficiency and precision of drone detection.