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.项目总结/摘要 中风是导致美国和其他国家严重、长期残疾的主要原因 经常锻炼或从事有规律的活动，中风复发的风险增加30%。一对一 由于保险规定和治疗师的原因， （身体和职业）经常规定以家庭为基础的锻炼计划。这些项目历史上 依从率低，并且患者报告通常可能有偏见、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，不能区分各种 活动深度传感器可以在家中使用，以检测福尔斯并监测老年人的家中步态模式。 成年人了其他研究人员已经使用深度传感器来检测和辨别实验室或模拟环境中的活动。 一个没有任何残疾的家庭环境。在本提案中， 和评估系统（DARAS）将合并以前的环境深度传感器工作与新开发的 算法，以客观和准确地测量中风患者的活动量和类型， 回家这将在三个具体目标中完成。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 将使用来自对中风个体（n =10）的基于实验室的测试的真实世界数据来训练算法。我们 将完善基于卷积神经网络（CNN）的算法，用于准确分割和 从深度视频的未修剪处理中识别活动。开发的活动识别系统 将在1年内部署在10名中风患者的家中。为了确定影响 关于日常生活和系统的可接受性以及所产生的数据，将举行由10名个人组成的焦点小组会议。 中风本提案中开发的DARAS将为各种 中风后干预，并提供职业治疗师的能力，以发现早期性能下降 并进行干预。