Wearable Sensors and AI to Recognize and Evaluate IADLs

可穿戴传感器和人工智能来识别和评估 IADL

• 批准号: 10432662
• 负责人: Keith Cole
• 金额: $ 20.19万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10432662
• 关键词: Activities of Daily Living; Adult; Affect; Age; Algorithms; Alzheimer' s disease related dementia; Artificial Intelligence; Attention; Biological Markers; Biomedical Engineering; Brain; Characteristics; Classification; Clinical; Cognition; Cognitive; Communities; Community Participation; Conflict (Psychology); Controlled Environment; Data; Databases; Dementia; Detection; Deterioration; Early Diagnosis; Early Intervention; Early identification; Elderly; Environment; Equilibrium; Functional disorder; Future; Gait; Gait speed; Goals; Health; Impaired cognition; Individual; Interdisciplinary Study; Knowledge; Label; Life; Machine Learning; Measurement; Measures; Medical Care Costs; Memory; Methods; Modeling; Monitor; Motor; Movement; Occupational Therapy; Performance; Physical Function; Physical therapy; Property; Resources; Safety; Sample Size; Sensitivity and Specificity; Series; Stream; Task Performances; Technology; Testing; Time; Unit of Measure; base; care systems; clinical practice; cognitive function; cognitive load; cognitive task; deep learning; deep learning algorithm; experience; fall injury; fall risk; feature extraction; human subject; improved; informal care; innovation; instrumental activity of daily living; kinematics; mild cognitive impairment; motor impairment; multimodality; prevent; recruit; research clinical testing; sensor; systematic review; therapy design; wearable device; wearable sensor technology

Project Summary/Abstract: Mild cognitive impairment (MCI) reportedly affects up to 24% of older adults and involves an associated decline in functional mobility. Individuals with MCI experience decreased balance, decreased gait speed, altered gait parameters, and even a greater risk of falling. Currently, clinical measures of balance and mobility only moderately predict dysfunction associated with MCI. Recent studies using cognitive-motor dual-tasks were promising. This is done by attempting to increase the complexity brain processing demand by combining a movement task, such as gait, with a cognitive task, such as counting down from a random number by 3's. Current studies exploring dual-task assessments offer conflicting results in their ability to detect MCI, limiting their reliability. We hypothesize that current clinical testing paradigms lack ecological validity and functional task performance. This oversight limits the complexity of performing self- selected movements and the associated cognitive overlay required for instrumental activities of daily living (IADLs) engagement. It may be this additional real-world complexity that results in performance difficulty due to MCI and/or altered functional movement. The objective of this project is to combine the expertise of physical and occupational therapy and biomedical engineering to use advancing wearable technology of inertial measurement units (IMU) and advanced deep learning algorithms to develop a framework for recognizing and determining ability to perform naturalistic movements in an ecologically valid setting. To accomplish this, we will recruit individuals with MCI (n=15) and cognitively healthy (n=15) adults from 60-75 years old to perform a simulated IADL involving a series of tasks that include at least 10 repetitions of discrete activities that are involved in typical grocery shopping (e.g. carrying a basket, reaching up for an item, etc.). IMU data will be labeled using video ground truth, allowing files consisting of a full activity stream (the complete grocery shopping task) as well as files segregating discrete activities (retrieving a can of soup from a shelf). We will then develop and validate a deep learning framework in order to identify each discrete activity performed in the IADL task in both those with MCI and cognitively normal older adults (Aim 1). Additionally, we will use feature extraction methods to identify specific kinematic performance parameters of each gait and non-gait based activity (Aim 2). We then use this pilot kinematic data to identify sample sizes of future studies with adequate power and effect size to provide a robust framework to use naturalistic movements to detect movement dysfunction in those with MCI. By achieving these aims, we establish a state-of-the-art framework that may ultimately be used for detecting and measuring performance and safety of IADL engagement in older adults. Our long-term goal is to develop a naturalistic and highly reliable method that may provide early identification of cognitive and movement dysfunction in order to initiate treatment before the onset of dementia, as well as to provide a functional test to measure potential longitudinal functional changes.

项目摘要/摘要：据报道，轻度认知障碍（MCI）影响多达24％的老年人和 涉及功能流动性的相关下降。具有MCI经验的个人降低了平衡， 步态速度降低，步态参数改变，甚至更大的跌落风险。目前，临床措施 平衡和移动性仅适度预测与MCI相关的功能障碍。最近使用的研究 认知运动双重任务是有希望的。这是通过试图增加复杂性大脑来完成的 通过将运动任务（例如步态）与认知任务相结合来处理需求，例如计算 从3个随机数中。当前探索双重任务评估的研究提供了相互矛盾的结果 能够检测MCI，限制其可靠性。我们假设目前缺乏临床测试范例 生态有效性和功能任务绩效。这种疏忽限制了执行自我的复杂性 选定的动作以及日常生活乐器活动所需的相关认知覆盖 （IADLS）参与。可能是这种额外的现实世界复杂性，导致性能难度 MCI和/或功能变化。该项目的目的是结合物理的专业知识 以及职业疗法和生物医学工程，以使用惯性的可穿戴技术 测量单元（IMU）和先进的深度学习算法，以开发识别和 确定在生态有效的环境中执行自然运动的能力。为此，我们 将招募有MCI（n = 15）和认知健康（n = 15）的人的60-75岁成年人 模拟的IADL涉及一系列任务，其中包括至少10个离散活动的重复 参与典型的杂货店购物（例如携带篮子，伸手去拿等）。 IMU数据将是 使用视频地面真相标记，允许由完整活动流组成的文件（完整的杂货店 购物任务）以及文件隔离离散活动（从架子上检索一罐汤）。我们将 然后开发和验证一个深度学习框架，以确定在 IADL的任务是具有MCI和认知正常老年人的人（AIM 1）。此外，我们将使用功能 提取方法来识别每个步态和非对基因的特定运动学性能参数 活动（目标2）。然后，我们使用此试点运动数据来识别未来研究的样本量 功率和效果大小可提供强大的框架，以使用自然主义运动来检测运动 患有MCI的人的功能障碍。通过实现这些目标，我们建立了一个最先进的框架 最终可用于检测和测量老年人IADL参与的性能和安全性。 我们的长期目标是开发一种自然主义且高度可靠的方法，该方法可能会提供早期识别 认知和运动功能障碍，以便在痴呆发作之前开始治疗 提供功能测试以测量潜在的纵向功能变化。