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
癌症患者在治疗期间和治疗后经历实质性的功能障碍,通常需要
康复和锻炼干预。然而,对特定缺陷和损伤的功能评估,
需要个体告知运动疗法。肿瘤学工作人员开展此类活动的能力面临挑战
功能评估,因此限制了康复提供者识别和满足这些需求的能力。
个体这项工作旨在开发一种新颖的,低成本的基于PC/摄像机的图像/视频监控系统,可以a)
连续且不引人注目地监测癌症患者在他/她的自然环境中的身体健康,B)
确定功能缺陷的存在和程度,以及c)促进个性化康复干预,
为癌症患者护理提供风险分层和个性化的方法。根据患者的日常活动或
锻炼,客观和定量的临床相关的和实验室验证的身体表现的测量(例如,
步态速度,平衡和跌倒风险)将通过使用最先进的图像处理和机器学习来提取
技术,涉及人体检测,姿势估计,深度学习算法和跌倒风险预测模型。
通过基于PC的
软件和门户网站,该系统将促进积极的康复,通过临床医生批准,
个性化的保健提示和锻炼建议。
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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