Peripheral Microneedle Patch for First-Aid Hemostasis.

Despite the minimized puncture sizes and high efficiency, microneedle (MN) patches have not been used to inject hemostatic drugs into bleeding wounds because they easily destroy capillaries when a tissue is pierced. In this study, a shelf-stable dissolving MN patch is developed to prevent rebleeding during an emergency treatment. A minimally and site-selectively invasive hemostatic drug delivery system is established by using a peripheral MN (p-MN) patch that does not directly intrude the wound site but enables topical drug absorption in the damaged capillaries. The invasiveness of MNs is histologically examined by using a bleeding liver of a Sprague-Dawley (SD) rat as an extreme wound model in vivo. The skin penetration force is quantified to demonstrate that the administration of the p-MN patch is milder than that of the conventional MN patch. Hemostatic performance is systematically studied by analyzing bleeding weight and time and comparing them with that of conventional hemostasis methods. The superior performance of a p-MN for the heparin-pretreated SD rat model is demonstrated by intravenous injection in vivo.

Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data.

To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the measurement noise for Kalman filtering, which is one of the most representative filtering techniques. Kalman filtering corrects inaccurate values of input sensor data, and its filtering performance varies depending on the input noise parameters. In particular, if the noise parameters determined based on the user's experience are incorrect, the accuracy of Kalman filtering may be reduced significantly. Based on this observation, this paper addresses how to determine the measurement noise variance, a major input parameter of Kalman filtering, by analyzing past sensor data and how to use the estimated noise to improve the filtering accuracy. More specifically, to estimate the measurement noise variance, two analytical methods are proposed: one a transform-based method using a wavelet transform and the other a learning-based method using a denoising autoencoder. Experimental results show that the proposed methods estimated the measurement noise variance accurately and were superior to the experience-based method in the filtering accuracy.