Wearable Sensors and AI to Recognize and Evaluate IADLs

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 涉及到功能移动性的相关下降。患有MCI的人平衡能力下降， 步态速度减慢，步态参数改变，甚至有更大的摔倒风险。目前，临床措施 平衡和活动能力只能适度预测与MCI相关的功能障碍。最近的研究使用 认知-运动双重任务前景看好。这是通过尝试增加大脑的复杂性来实现的 通过将运动任务(如步态)与认知任务(如倒计时)相结合来处理需求 目前探索双重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例缺乏 生态效度和功能任务绩效。这种疏忽限制了执行自我操作的复杂性 日常生活中工具性活动所需的选定动作和相关的认知覆盖 (IADLS)接洽。可能正是这种额外的现实世界复杂性导致了性能困难，这是由于 MCI和/或改变的功能性运动。该项目的目标是将物理方面的专业知识 以及职业治疗和生物医学工程使用先进的可穿戴惯性技术 测量单位(IMU)和高级深度学习算法，以开发识别和 确定在生态有效的环境中进行自然主义运动的能力。为了实现这一目标，我们 将招募患有MCI(n=15)和认知健康(n=15)的60-75岁成年人进行 模拟IADL涉及一系列任务，其中包括至少10个重复的离散活动，这些活动 参与典型的杂货店购物(例如提篮子、伸手拿东西等)。IMU数据将是 使用视频地面事实进行标记，允许文件包含完整的活动流(完整的杂货 购物任务)以及分离离散活动的文件(从货架上取回一罐汤)。我们会 然后开发和验证深度学习框架，以确定在 MCI患者和认知正常老年人的IADL任务(目标1)。此外，我们还将使用功能 识别每个步态和非步态特定运动学性能参数的提取方法 活动(目标2)。然后，我们使用该飞行员运动学数据来确定未来研究的样本量 力量和效果大小，以提供一个健壮的框架，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，可以 最终用于检测和测量老年人参与IADL的表现和安全性。 我们的长期目标是开发一种自然主义的、高度可靠的方法，可以提供早期识别 认知和运动功能障碍，以便在痴呆症发作之前开始治疗，以及 提供功能测试以衡量潜在的纵向功能变化。

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.