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

自动视频监控算法开发可减少医院从椅子出口跌倒的情况

基本信息

批准号：
10081180
负责人：
Lucas Sabalka
金额：
$ 15万
依托单位：
OCUVERA LLC
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10081180
关键词：
3-Dimensional Address Adult Affect Algorithm Design Algorithms Applications Grants Beds Behavior Control Groups Custom Data Databases Detection Development Effectiveness Elderly Fall prevention Frequencies Future Goals Health Care Costs Health care facility Hospital Nursing Staff Hospitals Hour Injury Intervention Knowledge Lead Modeling Monitor Movement Nurses Nursing Staff Patient Monitoring Patient-Focused Outcomes Patients Patients&apos Rooms Performance Postural adjustments Quality of life Reporting Research Proposals Risk Running Sampling Side Small Business Innovation Research Grant Source System Telephone Testing Time Wheelchairs algorithm development base behavior change cost design experience fall injury fall risk falls handheld mobile device human old age (65+)improved improved outcome innovative technologies member novel patient privacy phase 1 study prediction algorithm prevent simulation smart watch statistics

项目摘要

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.

项目摘要建议的SBIR项目的目标是证明开发自定义算法的可行性减少与医院环境中无人协助的椅子出口相关的跌倒风险。该项目将重点放在提前预测无人协助的病人座椅出口，为护理人员提供额外的准备时间满足患者需求并防止潜在的跌倒情况。该系统将使用来自三维空间的数据 (3D)视频处理，以开发算法来监控患者移动的变化，以预测何时在不久的将来，一名患者将从椅子上下来。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，该数据库记录了从先前研究中获得的三维患者视频；检测和建模各种类型的椅子；设计新的预测和检测出口的算法坐在椅子上。结果包括使用Ocuvera专利对算法性能进行模拟的报告算法测试系统。如果成功，这项研究将展示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，这是降低跌倒比率的重要机会并改善患者、医院和护理人员的预后。