Development and Acceptability of an Ambient In-Home Activity Assessment Tool for Stroke
Development and Acceptability of an Ambient In-Home Activity Assessment Tool for Stroke
批准号:
9804369
负责人:
Rachel Marie Proffitt
金额:
$18.66万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-10 至 2021-08-31
关键词:
AcuteAdherenceAlgorithmsAssessment toolCaringChronicChronic PhaseClientClinicalClinics and HospitalsCommunitiesDataDevelopmentElderlyEnvironmentExerciseFatigueFocus GroupsGaitGoalsGoldHealthcareHome environmentImageIndividualInsuranceInterventionLaboratoriesLifeMeasurementMeasuresMonitorMotivationMotorNetwork-basedNeurologicOccupationalOccupational TherapistOutcomeOutcome AssessmentPainParticipantPatient Self-ReportPatientsPatternPerformancePopulationProcessRecurrenceRegulationRehabilitation therapyReportingResearchResearch PersonnelRiskStrokeSystemTechnologyTestingTimeTrainingUnited StatesWorkadherence ratebasecognitive functionconvolutional neural networkdisabilityexercise programexperiencefallshemiparesismonitoring devicenovelpost strokeprogramssensorstroke interventionsuccesstoolwearable device
中文摘要
1. 项目总结/文摘
英文摘要
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.
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会议论文
Reducing COVID-19 Related Disability in Rural Community-Dwelling Older Adults Using Smart Technology
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批准号:10360303
-
项目类别:
-
资助金额:$82.02万
-
财政年份:2021
-
负责人:Rachel Marie Proffitt
-
依托单位:
Reducing COVID-19 Related Disability in Rural Community-Dwelling Older Adults Using Smart Technology
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批准号:10688192
-
项目类别:
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资助金额:$64.96万
-
财政年份:2021
-
负责人:Rachel Marie Proffitt
-
依托单位:
Development and Acceptability of an Ambient In-Home Activity Assessment Tool for Stroke
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批准号:10016135
-
项目类别:
-
资助金额:$22.51万
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财政年份:2019
-
负责人:Rachel Marie Proffitt
-
依托单位:
海外基金