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

项目总结/摘要 许多福尔斯在护理设施没有报告在夜间时，老年人独自一人。它可以导致 生活质量下降，害怕跌倒和活动受限增加，功能能力下降，以及 增加受伤或死亡的风险。我们提出WARE-Care：基于毫米波的跌倒评估， pRevEntion是一个非侵入式系统，在夜间工作，收集和评估老年人的跌倒数据。 WARE-Care将提供精确的运动测量，结构紧凑，易于设置。的 该系统安全、私密且用户友好，适用于护理设施应用。该系统是强烈的兴趣， 护理中心，包括当地的手工犹太人老龄化服务中心。 WARE-Care系统是新颖独特的，基于PI在毫米波雷达方面的发展 结合先进的机器学习技术。毫米波雷达新领域开启机遇 用于老年人监测，具有显著的优势，可补充现有的视觉和身体佩戴传感器。 毫米波雷达可在所有照明条件下工作，并生成点云，可用于精确测量 在尊重隐私的情况下创建骨架，例如，不会收集详细的面部信息。非接触， 非侵入式传感器可以优选为电池有限的和潜在刺激性的身体佩戴传感器 老年人的睡眠时间我们将首先在不同的房间中构建和验证拟议的系统，包括 养老院里典型的洗手间和卧室然后，我们将应用新的基于神经网络的方法， 恢复精确的骨骼运动，检测人的异常行为，而不考虑年龄、性别、体重， 或者身高我们会先招募一批健康的成年人（18岁或以上）， 系统该系统将进一步在目标老年群体（65岁及以上）中进行验证，并与 可穿戴运动传感器一个跨学科的团队已经组装，以开发和执行初步的临床 验证这个新系统。

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.