Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Haemostatic materials for wound healing applications

Abstract: Wounds are one of the most common health issues, and the cost of wound care and healing has continued to increase over the past decade. The first step in wound healing is haemostasis, and the development of haemostatic materials that aid wound healing has accelerated in the past 5 years. Numerous haemostatic materials have been fabricated, composed of different active components (including natural polymers, synthetic polymers, silicon-based materials and metal-containing materials) and in various forms (including sponges, hydrogels, nanofibres and particles). In this Review, we provide an overview of haemostatic materials in wound healing, focusing on their chemical design and operation. We describe the physiological process of haemostasis to elucidate the principles that underpin the design of haemostatic wound dressings. We also highlight the advantages and limitations of the different active components and forms of haemostatic materials. The main challenges and future directions in the development of haemostatic materials for wound healing are proposed. Uncontrolled bleeding is a major cause of death, incentivizing the development of biomaterials that aid haemostasis and wound healing. This Review highlights the active components and forms of haemostatic materials, with a focus on their chemical design, and considers future trends in their development.

Fall Detection by Wearable Sensor and One-Class SVM Algorithm

Abstract: The fall is a crucial problem in the elderly people’s daily life, and the early detection of fall is very important to rescue the subjects and avoid the badly prognosis. In this paper, we use a wearable tri-axial accelerometer to capture the movement data of human body, and propose a novel fall detection method based on one-class support vector machine (SVM). The one-class SVM model is trained by the positive samples from the falls of younger volunteers and a dummy, and the outliers from the non-fall daily activities of younger and the elderly volunteers. The preliminary results show that this method can detect the falls effectively, and reduce the probability of being damaged in the experiments for the elderly people.