Ultra Wideband Fall Detection and Prediction Solution for People Living with Dementia

摘要： 有认知障碍的老年人跌倒的风险比没有认知障碍的老年人更高 减损。不幸的是，在痴呆症患者(PLWD)中检测跌倒或评估跌倒风险可能 由于收集自我报告信息或沟通功能测试的困难而具有挑战性 指示。拟议的项目将开发和测试自动坠落检测系统，该系统将使用Ultra 宽带(UWB)频段技术。UWB的优势，以及成熟的加速度计和 陀螺仪技术，是它比蓝牙(1-5米)或Wi-Fi产生更精确的分辨率(5-10厘米) (5-10米)。UWB的实时位置跟踪能力可以增强坠落检测的准确性和背景，并通过 呼叫警报系统缩短了响应时间。此外，拟议的系统将收集丰富的移动性数据，以 能够检测与活动相关的跌倒风险(例如，步态和平衡的变化)。因此，如果成功， 拟议的系统预计将简化和增强基于移动性的跌倒风险、跌倒检测和快速发送 关于PLWD的警报。在之前开发跌倒检测系统原型工作的基础上，这个快速通道应用程序 提出了从实验室研究到实际应用的两个阶段。第一阶段将测试以下能力 Theora®360，一种新型坠落检测系统，用于在实验室环境中检测模拟坠落，无论是传感器 位置对跌倒检测的准确性和用户的初始反馈有很大影响。继续进行的里程碑 下一阶段是在实验室环境中检测跌倒的90%的敏感性和90%的特异性，并对 3D定位处理、运动传感/的协议、初步算法和数据平台 分类和跌倒检测。第二阶段将评估Theora®360‘S检测基于活动能力的跌倒的能力 在PLWD中，并预测住在家里的60名护理接受者-照顾者双方随着时间的推移跌倒风险的变化。 使用之前建立的神经网络，总体活动度、步态特征和日常生活的变化 将遵循例行程序来开发活动建模和风险分析的算法。关于技术的反馈 将通过以下方式获得使用和用户满意度，包括对解决方案改进的建议 研究结束时的技术记录、可用性调查和访谈。因此，这项研究代表了一种 24/7全天候客观传感器数据的混合模型方法；功能评估、调查数据 在整个研究过程中收集了五次评估社会人口统计、护理和心理社会因素，以及 在研究接近尾声时收集定性可用性数据，以更好地了解评估的复杂性 这项新技术在PLWD及其照顾者中的跌倒风险和商业化潜力。预想的 作为Clairvoyant Networks和德克萨斯农工大学中心之间的企业-学术合作伙伴关系 社区健康和老龄化，这项建议利用了商业、老龄化、痴呆症、公共卫生、 临床科学、计算机科学和工程学，并将受益于杰出的 顾问组。

Abstract: Older adults with cognitive impairment experience an increased risk of falling than those without cognitive impairment. Unfortunately detecting falls or assessing fall risk among persons living with dementia (PLWD) can be challenging due to difficulties in collecting self-reported information or communicating functional test instructions. The proposed project will develop and test an automated fall detection system using Ultra Wideband (UWB) band technology. The advantage of UWB, along with well-established accelerometer and gyroscope technology, is that it produces a more precise resolution (5-10cm) than Bluetooth (1-5m) or Wi-Fi (5-10m). UWB’s real-time location tracking capacity can enhance fall detection accuracy and context, and with a call alert system reduce response time. In addition, the proposed system will collect rich mobility data to enable the detection of mobility-related fall risk (e.g., changes in gait and balance). Thus, if successful, the proposed system is expected to simplify and enhance mobility-based fall risk, fall detection, and quickly send alerts for PLWD. Building on prior work to develop a fall detection system prototype, this fast-track application proposes two phases moving from lab studies to real-world applications. Phase 1 will test the ability of Theora® 360, a novel fall detection system, to detect simulated falls in a laboratory setting, whether sensor location makes a difference in fall detection accuracy and initial user feedback. Milestones to proceed to the next phase are 90% sensitivity and 90% specificity in fall detection in a laboratory setting and codification of protocols, preliminary algorithms, and data platforms for 3D location processing, motion sensing/ categorization, and fall detection. Phase 2 will assess Theora® 360’s ability to detect mobility-based falls among PLWD and to predict changes in fall risk over time in 60 care-recipient-caregiver dyads living at home. Using a previously established neural network, changes in overall mobility, gait characteristics, and daily routines will be observed to develop algorithms for activity modeling and risk profiling. Feedback on technology use and user satisfaction including recommendations for solution improvement will be obtained through technology records, usability surveys, and interviews at the end of the study. Thus, the study represents a mixed model approach with objective sensor data on a 24/7 basis; functional assessments, survey data assessing sociodemographic, care, and psychosocial factors collected five times throughout the study, and qualitative usability data collected toward the end of the study to better understand the complexity of assessing fall risks and commercialization potential of this new technology among PLWD and their caregivers. Envisioned as a corporate-academic partnership between Clairvoyant Networks and Texas A&M University Center for Community Health and Aging, this proposal draws upon expertise in business, aging, dementia, public health, clinical sciences, computer sciences, and engineering, and will benefit from the input of a distinguished advisory group.