Calibrating free-living physical activity characteristics across functionally-limited populations using machine-learned accelerometer approaches

使用机器学习的加速度计方法校准功能受限人群的自由生活身体活动特征

• 批准号: 9899101
• 负责人: SCOTT J STRATH
• 金额: $ 57.28万
• 依托单位: UNIVERSITY OF WISCONSIN MILWAUKEE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9899101
• 关键词: Accelerometer; Activities of Daily Living; Address; Adopted; Adult; Affect; Age; Algorithms; Ankle; Area; Behavior; Behavior Therapy; Behavioral; Biomechanics; Calibration; Categories; Characteristics; Classification; Communities; Complex; Data; Data Collection; Disabled Persons; Disease; Disease Management; Dose; Effectiveness; Energy Metabolism; Environment; Evolution; Exercise; Frequencies; Funding; Grouping; Health; Hip region structure; Impairment; Individual; Intervention; Knowledge; Laboratories; Linear Regressions; Machine Learning; Measures; Medical Care Costs; Methods; Modeling; Movement; Movement Disorders; Outcome; Patients; Pattern; Physical Function; Physical activity; Physical assessment; Population; Population Heterogeneity; Prevalence; Process; Protocols documentation; Receiver Operating Characteristics; Research; Resolution; Sampling; Scientific Advances and Accomplishments; Site; Techniques; Technology; Testing; Time; Training; Validation; Work; Wrist; advanced analytics; algorithm training; base; computerized data processing; cost; disability; doubly-labeled water; evidence based guidelines; functional disability; functional group; innovation; machine learning algorithm; machine learning method; motor impairment; population movement; prediction algorithm; prevent; respiratory; response; sedentary lifestyle; sensor; standard measure; wearable sensor technology

PROJECT SUMMARY/ ABSTRACT One in 5 U.S. adults are thought to be living with a disability/impairment, a complex and multifaceted condition affecting movement patterns with related medical care costs exceeding $300 billion annually. Precise and accurate assessment of physical activity (PA) and sedentary behavior (SB) in individuals with disabiliy/ impairment is essential to accurately measure PA/SB prevalence rates and effectiveness of behavioral based PA/SB interventions, and to fully elucidate PA/SB dose-response health relationships. Scientific progress has been made in this area with advanced analytics and data processing techniques applied to wearable accelerometers from laboratory calibration studies. There is a scientific need to extend calibration studies from fixed-duration laboratory simulated activities of daily living to free-living calibrations with natural observation and accelerometer algorithm training and validation. The aims of this proposal fill this essential scientific knowledge gap. The specific aims are: 1) To evaluate and refine machine-learned algorithms to predict energy cost and activity type during a 24-hr respiratory calorimeter stay; 2) To validate machine-learned accelerometer algorithms with field-derived, video-recorded direct observation; and 3) To validate machine-learned algorithms using the doubly labeled water technique. Our highly qualified research team will address the above aims by using brief translatable functional tests to cluster movement-impaired populations into groups of healthy, upper-body impairment, lower-body impairment, and upper- and lower-body impairment. Best practice free- living calibration protocols will then be used to train, refine, and evaluate functional clustered-specific accelerometer algorithms for predicting activity energy cost, activity type, activity transitions, and activity domain. The results of these proposed studies will for the first time provide an innovative and translatable approach to categorize and assess free-living PA/SB in persons with disability and movement impairment.

项目摘要/摘要 美国五分之一的成年人被认为患有残疾/障碍，这是一种复杂而多方面的状况 每年影响运动模式，相关的医疗服务费用超过3000亿美元。精确和 精确评估体育活动（PA）和久坐行为（SB） 损伤对于准确测量PA/SB的患病率和基于行为的有效性至关重要 PA/SB干预措施，并充分阐明PA/SB剂量反应健康关系。科学进步已有 是在该领域使用高级分析和数据处理技术应用于可穿戴的领域制造的 实验室校准研究的加速度计。科学的需要扩展了从 自然观察 以及加速度计算法培训和验证。该提案的目的填补了这一基本科学 知识差距。具体目的是：1）评估和完善机器学习算法以预测能量 24小时呼吸量热仪停留期间的成本和活动类型； 2）验证机器学习的加速度计 具有现场衍生的，视频录制的直接观察算法； 3）验证机器学习算法 使用双重标记的水技术。我们高素质的研究团队将解决上述目标 使用简短的可翻译功能测试将受损的运动群集成成一组健康的人群 上体障碍，下体障碍以及上身和下半身障碍。最佳练习免费 - 然后，将使用生活校准方案来训练，完善和评估功能群集特异性 预测活动能源成本，活动类型，活动过渡和活动的加速度计算法 领域。这些拟议的研究的结果首次将提供创新和可翻译的结果 在残疾和运动障碍的人中对自由生活PA/SB进行分类和评估的方法。