WARE-Care: a novel RF-based system to assess and prevent falling

WARE-Care：一种基于射频的新型系统，用于评估和防止跌倒

基本信息

批准号：
10673041
负责人：
Siyang Cao
金额：
$ 21.63万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10673041
关键词：
3-Dimensional Adult Age Aging Agreement Alzheimer&apos s Disease Arizona Behavior Behavior monitoring Calibration Canes Care given by nurses Caring Cessation of life Charge Clinical Clinical Trials Communities Complement Data Databases Detection Development Devices Diagnostic Services Discipline of Nursing Disease Elderly Electromagnetic Energy Environment Evaluation Event Extremely High Frequency Radio Waves Face Fall prevention Forearm Gait Goals Health Height Hour Human Injury Laboratories Lighting Lower Extremity Machine Learning Maps Measurement Measures Medical Methods Modeling Monitor Motion Movement Network-based Nursing Homes Nursing Staff Participant Performance Persons Posture Prevention Privacy Privatization Probability Quality of life Radar Radiation Recovery Reporting Research Residual state Risk Risk Assessment Safety Sample Size Seizures Services Skeleton Sleep System Techniques Technology Testing Thigh structure Time Training Universities Unmarried person Upper Extremity Upper arm Validation Visual Walking Weight Work adverse outcome arm care costs care systems computer center cost data exchange deep learning design exposed human population fall risk falls fear of falling follow-up follower of religion Jewish frailty improved interest irritation kinematics learning network long short term memory meter motion sensor network architecture neural network neural network architecture novel pandemic disease portability privacy protection recruit response sensor sex signal processing skeletal movement smartphone application software development telehealth tool user-friendly walker wearable device wearable sensor technology wireless

3-Dimensional Adult Age Aging Agreement Alzheimer&apos s Disease Arizona Behavior Behavior monitoring Calibration Canes Care given by nurses Caring Cessation of life Charge Clinical Clinical Trials Communities Complement Data Databases Detection Development Devices Diagnostic Services Discipline of Nursing Disease Elderly Electromagnetic Energy Environment Evaluation Event Extremely High Frequency Radio Waves Face Fall prevention Forearm Gait Goals Health Height Hour Human Injury Laboratories Lighting Lower Extremity Machine Learning Maps Measurement Measures Medical Methods Modeling Monitor Motion Movement Network-based Nursing Homes Nursing Staff Participant Performance Persons Posture Prevention Privacy Privatization Probability Quality of life Radar Radiation Recovery Reporting Research Residual state Risk Risk Assessment Safety Sample Size Seizures Services Skeleton Sleep System Techniques Technology Testing Thigh structure Time Training Universities Unmarried person Upper Extremity Upper arm Validation Visual Walking Weight Work adverse outcome arm care costs care systems computer center cost data exchange deep learning design exposed human population fall risk falls fear of falling follow-up follower of religion Jewish frailty improved interest irritation kinematics learning network long short term memory meter motion sensor network architecture neural network neural network architecture novel pandemic disease portability privacy protection recruit response sensor sex signal processing skeletal movement smartphone application software development telehealth tool user-friendly walker wearable device wearable sensor technology wireless

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项目摘要

Project Summary/Abstract Many falls in nursing facilities are not reported during night when older adults are alone. It can lead to reduced quality of life, increased fear of falling and restriction of activities, decreased ability to function, and increased risk of injuries or death. We propose WARE-Care: mmWave based fall Assessment and pRevEntion, as a non-intrusive system to work during the night to collect and assess older adults’ falling data. WARE-Care will provide an accurate measurement of movement and be compact and easy to set up. The system is safe, private, and user-friendly for nursing facility applications. The system is of strong interest to the nursing centers, including the local Handmaker Jewish Services For Aging center. The WARE-Care system is novel and unique and is based on the PI’s developments in mmWave radar combined with advanced machine learning techniques. The new field of mmWave radar opens opportunities for elders monitoring with significant advantages that complement existing visual and body-worn sensors. mmWave radar operates in all lighting conditions and generates a point cloud that can be used to accurately create a skeleton while respecting privacy, e.g. no detailed facial information will be collected. The non-touch, non-obtrusive sensor can be preferred to battery limited and potentially irritating body worn sensors during sleeping hours for elders. We will first build and verify the proposed system in different rooms including a typical restroom and bedroom in the nursing home. We will then apply new neural-network based methods to recover accurate skeleton movement and detect abnormal behavior of people regardless of age, sex, weight, or height. We will first recruit a group of healthy adults (18 or older) for setting up and training the proposed system. The system will further be validated on a targeted older group (65 and older), and be compared with wearable motion sensors. An interdisciplinary team has been assembled to develop and perform initial clinical validation of this novel system.

项目摘要/摘要在老年人独自一人的夜晚，没有报道护士设施中的许多瀑布。它可以导致减少生活质量，增加对活动降低和限制活动的恐惧，功能能力降低以及增加受伤或死亡的风险。我们提出了Ware-Care：基于MMWave的秋季评估和预防，作为一种非侵入性系统，可以在夜间收集和评估老年人的数据下降。 Ware-Care将提供对运动的准确测量，并保持紧凑且易于设置。这对于护士设施应用程序，系统是安全，私人和用户友好的。该系统对护士中心，包括当地的熟悉中心犹太人服务。 Ware-Care系统是新颖而独特的，它基于PI在MMWave Radar中的发展结合先进的机器学习技术。 MMWave Radar的新领域开放了机会对于长者监视，具有重大优势，可以补充现有的视觉和磨损传感器。 MMWave雷达在所有照明条件下操作，并生成一个可用于准确的点云在尊重隐私时创建骨骼，例如不会收集详细的面部信息。非接触，非引人注目的传感器可以比电池有限且潜在的刺激性磨损传感器更优选长老的睡眠时间。我们将首先在不同的房间中构建并验证所提出的系统护士家中的典型洗手间和卧室。然后，我们将应用新的基于神经网络的方法恢复准确的骨骼运动并检测人的异常行为，无论年龄，性别，体重如何或高度。我们将首先招募一组健康的成年人（18岁以上）来建立和培训建议系统。该系统将在有针对性的旧组（65岁及以上）上进一步验证，并与可穿戴运动传感器。一个跨学科团队已经组装，以开发和执行初始临床验证这个新型系统。