TOPIC 409 PHASE I FY20 SBIR CONTRACT - TOPIC 409 - INTELLIGENT AUTOMATION INC.
TOPIC 409 PHASE I FY20 SBIR CONTRACT - TOPIC 409 - INTELLIGENT AUTOMATION INC.
批准号:
10267589
负责人:
MARK JAMES
金额:
$40.0万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-16 至 2021-06-15
关键词:
AftercareAutomationCancer PatientClinicComputer softwareContractsDetectionEnvironmentEquilibriumExerciseExercise TherapyFeedbackGait speedGoalsHumanHuman ActivitiesImageImpairmentIndividualInterventionIntuitionLaboratoriesMachine LearningMeasuresMonitorOncologyPatient CarePatientsPhasePhysical PerformancePhysical assessmentProcessProviderRecommendationRehabilitation therapyReportingRiskSmall Business Innovation Research GrantSymptomsSystemTechniquesTimeWorkbaseclinically relevantcostdeep learning algorithmdesignexercise interventionexercise rehabilitationexperiencefall riskfunctional disabilityimage processingmachine learning algorithmnovelpersonalized approachpersonalized health carephysical conditioningprototyperisk prediction modelsoftware systemsweb portal
中文摘要
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英文摘要
Cancer patients experience substantial functional impairments during and after treatments, often requiring
rehabilitation and exercise interventions. However, a functional assessment of the specific deficits and impairments in
the individual is needed to inform exercise therapy. The oncology workforce is challenged in its capacity to conduct such
functional assessment, therefore limiting the ability of rehabilitation providers to identify and meet the needs of these
individuals. This work seeks to develop a novel, low-cost PC/camera-based image/video-monitoring system that can a)
continuously and unobtrusively monitor the physical health of a cancer patient in his/her natural environment, b)
determine the existence and extent of functional deficits, and c) facilitate personalized rehabilitative interventions,
providing a risk-stratified and personalized approach to cancer patient care. Based on patient’s daily activities or
exercises, objective and quantitative clinically-relevant and laboratory-validated measures of physical performance (e.g.,
gait speed, balance, and fall risk) will be extracted by using state-of-the-art image-processing and machine-learning
techniques, involving human-detection, pose-estimation, and deep-learning algorithms and fall-risk prediction models.
By providing the patients and their clinicians with a real-time assessment of their physical performance, through a PCbased
software and a Web portal, respectively, the system will facilitate proactive rehabilitation via clinician-approved,
personalized health-care tips and exercise recommendations.
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