Development and Acceptability of an Ambient In-Home Activity Assessment Tool for Stroke

中风室内环境活动评估工具的开发和可接受性

• 批准号: 9804369
• 负责人: Rachel Marie Proffitt
• 金额: $ 18.66万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-10 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9804369
• 关键词: Acute; Adherence; Algorithms; Assessment tool; Caring; Chronic; Chronic Phase; Client; Clinical; Clinics and Hospitals; Communities; Data; Development; Elderly; Environment; Exercise; Fatigue; Focus Groups; Gait; Goals; Gold; Healthcare; Home environment; Image; Individual; Insurance; Intervention; Laboratories; Life; Measurement; Measures; Monitor; Motivation; Motor; Network-based; Neurologic; Occupational; Occupational Therapist; Outcome; Outcome Assessment; Pain; Participant; Patient Self-Report; Patients; Pattern; Performance; Population; Process; Recurrence; Regulation; Rehabilitation therapy; Reporting; Research; Research Personnel; Risk; Stroke; System; Technology; Testing; Time; Training; United States; Work; adherence rate; base; cognitive function; convolutional neural network; disability; exercise program; experience; falls; hemiparesis; monitoring device; novel; post stroke; programs; sensor; stroke intervention; success; tool; wearable device

1. Project Summary/Abstract Stroke is the leading cause of serious, long-term disability in the United States and those that do not exercise or engage in regular activity are at a 30% increased risk of experiencing a recurrent stroke. One-on- one rehabilitation sessions are frequently limited in number due to insurance regulations and therapists (physical and occupational) frequently prescribe home-based exercise programs. These programs historically have low adherence rates and patient report can often be biased, incomplete, or inaccurate. Wearable sensors can track amount of activity but these sensors are limited in scope and cannot discern between various activities. Depth sensors can be used in the home to detect falls and monitor in-home gait patterns of well older adults. Other researchers have used depth sensors to detect and discern activities in laboratories or mock home environments with a population without any disabilities. In this proposal, the Daily Activity Recognition and Assessment System (DARAS) will merge prior ambient depth sensor work with newly developed algorithms to objectively and accurately measure the amount and type of activity of people with stroke living at home. This will be completed in three specific aims. The DARAS algorithms will be developed and refined for recognizing activities of people with stroke in the kitchen environment using the Foresite depth sensor. These algorithms will be trained using real-world data from lab-based testing with individuals with stroke (n =10). We will refine the Convolutional Neural Networks (CNN) based algorithm for accurately segmenting and recognizing activities from untrimmed processing of depth videos. The developed activity recognition system will be deployed in the homes of 10 individuals with stroke over the course of 1 year. To determine the impact on daily life and acceptability of the system and generated data, focus groups will be held with 10 individuals with stroke. The DARAS developed in this proposal will provide a novel outcome assessment for a variety of post-stroke interventions and provide occupational therapists the ability to detect declines in performance early on and intervene.

1. 项目概要/摘要 在美国和其他国家，中风是导致严重、长期残疾的主要原因 锻炼或从事常规活动会使中风复发的风险增加 30%。一对一—— 由于保险法规和治疗师的原因，一次康复治疗的次数经常受到限制 （身体和职业）经常制定家庭锻炼计划。这些节目在历史上 依从率低，患者报告通常可能有偏差、不完整或不准确。可穿戴传感器 可以跟踪活动量，但这些传感器的范围有限，无法区分各种活动 活动。深度传感器可用于家庭中检测跌倒并监控老年人的家庭步态模式 成年人。其他研究人员已经使用深度传感器来检测和识别实验室或模拟中的活动 人口没有任何残疾的家庭环境。在该提案中，日常活动识别 和评估系统（DARAS）将把之前的环境深度传感器工作与新开发的结合起来 客观准确地测量生活在中风患者活动量和类型的算法 家。这将通过三个具体目标来完成。 DARAS 算法将被开发和完善 使用 Foresite 深度传感器识别厨房环境中中风患者的活动。这些 将使用来自中风患者（n = 10）的实验室测试的真实数据来训练算法。我们 将完善基于卷积神经网络（CNN）的算法，以实现准确的分割和 从未经修剪的深度视频处理中识别活动。开发的活动识别系统 将在一年内部署到 10 名中风患者的家中。确定影响 关于日常生活以及系统和生成数据的可接受性，将举行由 10 人参加的焦点小组会议 伴有中风。本提案中开发的 DARAS 将为各种领域提供新颖的结果评估 中风后干预，并为职业治疗师提供早期发现表现下降的能力 并进行干预。