Continuous Fall Risk Monitoring System: Walking vs Activities of Daily Living

连续跌倒风险监测系统：步行与日常生活活动

• 批准号: 8199136
• 负责人: Amy Papadopoulos
• 金额: $ 14.73万
• 依托单位: AFRAME DIGITAL, INC.
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2012-11-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8199136
• 关键词: Acceleration; Accident and Emergency department; Activities of Daily Living; Adherence; Admission activity; Age; Area; Assisted Living Facilities; Caring; Cessation of life; Characteristics; Climacteric; Data; Devices; Early treatment; Elderly; Environment; Equilibrium; Etiology; Evaluation; Fall prevention; Frequencies; Gait; Gait abnormality; Goals; Health Care Costs; Home environment; Hospitals; Hour; Independent Living; Individual; Information Systems; Injury; Intervention; Laboratories; Learning; Leg; Location; Machine Learning; Measurement; Measures; Medical; Medical History; Methods; Middle Insomnia; Monitor; Periodicity; Persons; Pharmaceutical Preparations; Phase; Physical therapy; Physiologic Monitoring; Population; Probability; Provider; Quality of life; Receiver Operating Characteristics; Recording of previous events; Recruitment Activity; Research; Series; Specificity; System; Techniques; Technology; Testing; Time; Trauma; United States; Universities; Video Recording; Videotape; Virginia; Vision; Visit; Walking; Wrist; base; behavior change; fall risk; falls; improved; monitoring device; phase 1 study; sensor; volunteer

DESCRIPTION (provided by applicant): Falls are the leading cause of injury-related visits to U.S. emergency departments and the primary etiology of accidental deaths in persons over the age of 65 years1. Interventions such as physical therapy, adjusting medications, or behavior changes can reduce the elderly fall rate2. Fall risk determination is needed to identify who may benefit from interventions. Changes in fall risk may occur suddenly or gradually, and are more likely to become apparent in the home environment as an individual goes about their normal activities of daily living (ADLs) rather than during a limited and periodic care provider assessment. Consequently, a continuous, all-in-one system that would monitor elderly individuals in the home for signs they are becoming more susceptible to falls is needed to reduce falls in the elderly. To be effective, the monitoring device must be non-intrusive and socially acceptable. The long-term goal of this project is to [extend an existing non-intrusive, commercially available monitoring system capable of location tracking, physiologic monitoring, and alerting to also assess fall risk in real time; the system would consider factors such as frequent bathroom use, fitful sleep and changes in gait characteristics]. The monitor for the proposed research has a watch form factor, is worn at the wrist, and has demonstrated high acceptance rates by elderly users. Research has shown that abnormal gait is indicative of fall risk, leading to the use of a variety of measurements of gait in fall risk determinations. To analyze gait from data gathered during ADLs, it is necessary to differentiate periods of walking, which can then be analyzed for abnormal characteristics. The purpose of this Phase I proposal is to test the [feasibility of using tri-axial acceleration data gathered from a commercially available wrist monitor to recognize periods of walking. Walking data can then be used in conjunction with other system data to] make inferences about changes in fall risk. Thirty elderly (aged 65 and over), ambulatory volunteers residing in an independent living facility will be recruited. The volunteers will be asked to engage in normal ADLs while being monitored over a 4-hour time period. During the study, volunteers will be videotaped, monitored using the wrist device, and monitored using a body-area sensor network technology developed at the University of Virginia. The specific aims of this project will be to: 1) determine if it is feasible to distinguish between periods of walking and other ADLs using the presence of frequencies generally associated with walking in wrist- gathered acceleration data as the differentiator, 2) determine whether individuals typically demonstrate a narrower range of walking frequencies than that suggested for the entire population, and 3) determine if it is feasible to use machine learning and time-series techniques to distinguish between periods of walking and other ADLs using characteristics learned from walking and non-walking data. PUBLIC HEALTH RELEVANCE: Falls are the leading cause of injury-related visits to emergency departments in the United States and the primary cause of accidental deaths in persons over age 651. In order to reduce the number of falls, a continuous, non-intrusive, convenient monitoring system in an acceptable form factor which will monitor elderly individuals in their home environment for signs they are becoming more susceptible to falls needs to be developed. The long-term goal of this project is to develop such a system; the proposed project would take the necessary step of identifying and differentiating between walking and other normal activities [so that data gathered during walking can be used to recognize changes in stability for an individual, and then can be used in conjunction with other system data already being automatically collected in real-time to recognize an increased probability of falling both during walking or other activities.]

描述（由申请人提供）：跌倒是美国急诊科因受伤就诊的主要原因，也是 65 岁以上人群意外死亡的主要原因1。物理治疗、调整药物或行为改变等干预措施可以降低老年人跌倒率2。需要确定跌倒风险以确定谁可以从干预措施中受益。跌倒风险的变化可能突然或逐渐发生，并且在家庭环境中当个人进行正常的日常生活活动 (ADL) 时更可能变得明显，而不是在有限和定期的护理提供者评估期间。因此，需要一种连续的一体化系统来监测家中的老年人是否有更容易跌倒的迹象，以减少老年人跌倒的情况。为了有效，监控设备必须是非侵入性的并且为社会所接受。该项目的长期目标是[扩展现有的非侵入式商用监控系统，该系统能够进行位置跟踪、生理监测和警报，以实时评估跌倒风险；该系统会考虑诸如频繁使用卫生间、断断续续的睡眠和步态特征的变化等因素]。这项研究的显示器具有手表的外形，佩戴在手腕上，并且已被老年用户证明了很高的​​接受率。研究表明，异常步态预示着跌倒风险，因此在跌倒风险确定中使用了多种步态测量方法。为了根据 ADL 期间收集的数据分析步态，有必要区分行走的时间段，然后可以分析异常特征。该第一阶段提案的目的是测试[使用从市售手腕监视器收集的三轴加速度数据来识别行走时间段的可行性。然后，步行数据可以与其他系统数据结合使用，以推断跌倒风险的变化。将招募30名居住在独立生活设施中的老年人（65岁及以上）、流动志愿者。志愿者将被要求进行正常的日常生活活动，同时接受 4 小时的监测。在研究过程中，志愿者将被录像，使用腕式设备进行监控，并使用弗吉尼亚大学开发的身体区域传感器网络技术进行监控。该项目的具体目标是：1) 确定是否可以使用手腕收集的加速度数据中通常与步行相关的频率作为区分因素来区分步行时段和其他 ADL，2) 确定个体通常表现出的步行频率范围是否比整个人群建议的范围更窄，以及 3) 确定使用机器学习和时间序列技术来区分步行时段和其他 ADL 是否可行。 ADL 使用从步行和非步行数据中学到的特征。 公共健康相关性：在美国，跌倒是因受伤而前往急诊室就诊的主要原因，也是 651 岁以上老年人意外死亡的主要原因。为了减少跌倒次数，需要开发一种外形尺寸可接受的连续、非侵入式、方便的监测系统，该系统将监测老年人在家庭环境中是否有更容易跌倒的迹象。该项目的长期目标是开发这样一个系统；拟议项目将采取必要步骤识别和区分步行和其他正常活动[以便步行期间收集的数据可用于识别个人稳定性的变化，然后可与已实时自动收集的其他系统数据结合使用，以识别步行或其他活动期间跌倒概率的增加]。