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

针对痴呆症患者的超宽带跌倒检测和预测解决方案

基本信息

批准号：
10760690
负责人：
Shelley L Symonds
金额：
$ 49.59万
依托单位：
CLAIRVOYANT NETWORKS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-18 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10760690
关键词：
3-Dimensional Accelerometer Address Age Aging Algorithms Alzheimer&apos s disease related dementia Big Data Bluetooth Businesses Caregiver Burden Caregivers Caring Categories Characteristics Clinical Clinical Research Clinical Sciences Cognition Collaborations Communication Communities Data Dementia Detection Drops Elderly Engineering Ensure Equilibrium Feedback Gait Gait speed Home Hospitals Human Impaired cognition Instruction Interview Laboratories Location Manikins Mission Modeling Monitor Motion Movement Neighborhood Health Center Participant Patient Self-Report Patients Pattern Persons Phase Physical activity Population Prevalence Protocols documentation Psychosocial Factor Public Health Quality of life Reaction Time Recommendation Records Resolution Resources Risk Risk Assessment Sensitivity and Specificity Specificity Surveys System Technology Testing Texas Time Universities Work aging in place care giving burden care recipients caregiving commercialization community engagement computer science data communication detection platform experience fall injury fall risk falls field study human subject improved improved mobility innovation meter mild cognitive impairment motion sensor national surveillance neural network new technology novel prototype real world application risk prediction satisfaction sensor sensor technology sociodemographics success surveillance data tool usability user-friendly wearable sensor technology wireless fidelity

项目摘要

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.

抽象的：患有认知障碍的老年人比没有认知障碍的老年人跌倒的风险更高不幸的是，检测痴呆症患者 (PLWD) 的跌倒或评估跌倒风险可能会造成损害。由于收集自我报告信息或传达功能测试的困难而具有挑战性拟议项目将使用 Ultra 开发和测试自动跌倒检测系统。宽带 (UWB) 频段技术的优势以及成熟的加速度计和技术。陀螺仪技术的优点是它能产生比蓝牙（1-5m）或Wi-Fi更精确的分辨率（5-10cm） (5-10m) UWB 的实时位置跟踪能力可以提高跌倒检测的准确性和背景信息，并具有此外，建议的系统将收集丰富的移动数据，以减少响应时间。能够检测与移动相关的跌倒风险（例如步态和平衡的变化）。因此，如果成功，所提出的系统预计将简化和增强基于移动性的跌倒风险、跌倒检测并快速发送此快速通道应用程序以先前开发跌倒检测系统原型的工作为基础。提出从实验室研究到实际应用的两个阶段，第一阶段将测试其能力。 Theora® 360 是一种新颖的跌倒检测系统，用于检测实验室环境中的模拟跌倒，无论传感器是否位置会影响跌倒检测的准确性和进入里程碑的初始用户反馈。下一阶段是在实验室环境中跌倒检测的灵敏度为 90%，特异性为 90%，并编纂用于 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 2 阶段将评估 Theora® 360 检测基于移动性的跌倒的能力。对 PLWD 进行研究，并预测 60 名居家护理者、接受者和护理者二人组跌倒风险随时间的变化。使用先前建立的神经网络，整体移动性、步态特征和日常活动的变化将遵守惯例来开发活动建模和技术反馈的算法。使用和用户满意度，包括解决方案改进的建议，将通过以下方式获得技术记录、可用性调查和研究结束时的访谈因此，该研究代表了一个。基于 24/7 功能评估、调查数据的混合模型方法；评估整个研究过程中五次收集的社会人口统计、护理和社会心理因素，以及在研究结束时收集的定性可用性数据，以更好地理解评估的复杂性这项新技术在 PLWD 及其护理人员中的下降风险和商业化潜力。作为 Clairvoyant Networks 和德克萨斯 A&M 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，该提案借鉴了商业、老龄化、痴呆症、公共卫生、临床科学、计算机科学和工程学，并将受益于杰出人士的投入咨询小组。