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）影响高达24%的老年人， 包括相关的功能活动性下降。MCI患者的平衡能力下降， 步态速度降低，步态参数改变，甚至更大的跌倒风险。目前，临床措施 平衡和活动性的改变只能适度预测MCI相关的功能障碍。最近的研究使用 认知-运动双重任务是有希望的。这是通过尝试增加大脑的复杂性来实现的 通过将诸如步态的运动任务与诸如倒计时的认知任务相结合来处理需求 从一个随机数乘以3目前探索双任务评估的研究在他们的研究中提供了相互矛盾的结果。 检测MCI的能力，限制了它们的可靠性。我们假设目前的临床测试范式缺乏 生态效度和功能任务绩效。这种疏忽限制了自我执行的复杂性， 日常生活工具性活动所需的选定动作和相关认知覆盖 （IADL）参与。可能正是这种额外的实际复杂性导致了性能困难， MCI和/或功能性运动改变。该项目的目标是将物理学的专业知识联合收割机 职业治疗和生物医学工程，使用先进的惯性可穿戴技术， 测量单元（IMU）和先进的深度学习算法，以开发识别和 决定在生态有效的环境中进行自然运动的能力。为了做到这一点，我们 将招募60-75岁的MCI（n=15）和认知健康（n=15）成年人， 模拟IADL涉及一系列任务，包括至少10次重复的离散活动， 参与典型的杂货店购物（例如，提着篮子，伸手去拿物品等）。IMU数据将 使用视频地面实况进行标记，允许文件包含完整的活动流（完整的杂货店 购物任务）以及分离离散活动（从货架上取回一罐汤）的文件。我们将 然后开发并验证深度学习框架，以识别在 MCI患者和认知正常老年人的IADL任务（目标1）。此外，我们还将使用 提取方法，以识别每个步态和非步态的特定运动学性能参数 活动（目标2）。然后，我们使用这个试点运动学数据，以确定未来研究的样本量， 功效和效应大小，以提供一个强大的框架，使用自然运动来检测运动 MCI患者的功能障碍。通过实现这些目标，我们建立了一个最先进的框架， 最终用于检测和测量老年人参与IADL的性能和安全性。 我们的长期目标是开发一种自然的和高度可靠的方法，可以提供早期识别 认知和运动功能障碍，以便在痴呆症发作前开始治疗，以及 提供功能测试以测量潜在的纵向功能变化。