Automated Video Monitoring Algorithm Development to Reduce Falls in Hospitals from Chair Exits

项目摘要 拟议SBIR项目的目标是证明开发定制算法的可行性， 降低医院环境中与无辅助座椅退出相关的福尔斯风险。该项目将侧重于 在无人协助的患者座椅退出发生之前预测它们，为护理人员提供额外的前置时间， 满足患者需求并防止潜在的跌倒情况。该系统将使用三维数据 (3D)视频处理，开发算法，监测患者运动的变化， 不久的将来，一个病人将离开椅子。具体目标包括：识别包含患者座椅出口的视频 来自Ocuvera的数据库，该数据库记录了从以前的研究中获得的三维患者视频; 检测和模拟各种类型的椅子;设计新的算法来预测和检测出口 从椅子上。结果包括使用Ocuvera专有的 算法测试系统如果成功，这项研究将显示Ocuvera作为一项创新技术的潜力。 能够减少无人值守的座椅出口，这是降低跌倒率的重要机会 并改善患者、医院和护理人员的治疗效果。

Project Summary The goal of the proposed SBIR project is to demonstrate the feasibility of developing custom algorithms to reduce the risk of falls associated with unassisted chair exits in hospital settings. The project will focus on predicting unassisted patient chair exits before they occur to provide nursing staff with additional lead time to address patient needs and prevent potential fall situations. The system will use data from three-dimensional (3D) video processing to develop algorithms that will monitor for changes in patient movement to predict when a patient will exit a chair in the near future. Specific Aims include: identifying video containing patient chair exits from Ocuvera’s database of recorded three-dimensional patient video obtained from previous studies; detecting and modeling chairs of various types; and designing new algorithms for predicting and detecting exits from chairs. Results include reporting of simulations of algorithm performance using Ocuvera’s proprietary algorithm-testing system. If successful, this study would show Ocuvera’s potential as an innovative technology with the ability to reduce unattended chair exits, which represents a significant opportunity to reduce fall rates and improve outcomes for patients, hospitals, and nursing staff.