User interface for injury prediction : Focusing on car crashes

Sammanfattning

This thesis explores the intersection of safety technology, healthcare, and user interface design in the context of injury prediction services, focusing on post-injury prediction. The objective of this research is to enhance the speed and effectiveness of healthcare professionals' responses by evaluating current injury prediction technology and proposing a new user interface design targeted at injury prediction goals. The research examines the challenges and opportunities for communicating essential information to healthcare staff for life-saving actions in the treatment of car accident victims. To better understand the study's objectives and challenges, a mixed-methods research methodology was employed, including interviews with Autoliv personnel and healthcare practitioners. In car accidents, where injury prediction is critical for managing potential dangers, there is a lack of user interfaces for accurate prediction. This project aims to provide a strategy for developing a user interface that leverages advancements in artificial intelligence and machine learning techniques, with the goal of improving the accuracy and efficiency of injury predictions. These interfaces facilitate the communication of critical information to emergency medical providers following automobile accidents. The study emphasizes the necessity of collaborating with healthcare specialists to develop a conceptual framework that addresses post-crash challenges while enhancing resource allocation and treatment options. Finally, this study offers important insights into safety technology, healthcare innovation, and user interface design, paving the way for improved emergency response strategies and patient outcomes in injury prediction services.