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
基本信息
- 批准号:10432662
- 负责人:
- 金额:$ 20.19万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Activities of Daily LivingAdultAffectAgeAlgorithmsAlzheimer&aposs disease related dementiaArtificial IntelligenceAttentionBiological MarkersBiomedical EngineeringBrainCharacteristicsClassificationClinicalCognitionCognitiveCommunitiesCommunity ParticipationConflict (Psychology)Controlled EnvironmentDataDatabasesDementiaDetectionDeteriorationEarly DiagnosisEarly InterventionEarly identificationElderlyEnvironmentEquilibriumFunctional disorderFutureGaitGait speedGoalsHealthImpaired cognitionIndividualInterdisciplinary StudyKnowledgeLabelLifeMachine LearningMeasurementMeasuresMedical Care CostsMemoryMethodsModelingMonitorMotorMovementOccupational TherapyPerformancePhysical FunctionPhysical therapyPropertyResourcesSafetySample SizeSensitivity and SpecificitySeriesStreamTask PerformancesTechnologyTestingTimeUnit of Measurebasecare systemsclinical practicecognitive functioncognitive loadcognitive taskdeep learningdeep learning algorithmexperiencefall injuryfall riskfeature extractionhuman subjectimprovedinformal careinnovationinstrumental activity of daily livingkinematicsmild cognitive impairmentmotor impairmentmultimodalitypreventrecruitresearch clinical testingsensorsystematic reviewtherapy designwearable devicewearable sensor technology
项目摘要
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)影响了高达24%的老年人和老年人
项目成果
期刊论文数量(0)
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Keith Cole其他文献
Keith Cole的其他文献
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{{ truncateString('Keith Cole', 18)}}的其他基金
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:
10626772 - 财政年份:2022
- 资助金额:
$ 20.19万 - 项目类别:
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