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: 9284636
• 负责人: SCOTT J STRATH
• 金额: $ 56.62万
• 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/9284636
• 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; 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; base; computerized data processing; cost; disability; doubly-labeled water; evidence based guidelines; functional disability; functional group; innovation; learning strategy; motor impairment; population movement; prediction algorithm; prevent; respiratory; response; sedentary lifestyle; sensor; standard measure

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.

项目总结/摘要 五分之一的美国成年人被认为患有残疾/损伤，这是一种复杂而多方面的疾病 影响到流动模式，相关的医疗保健费用每年超过3 000亿美元。精确和 准确评估残疾人的身体活动（PA）和久坐行为（SB）/ 损伤对于准确测量PA/SB患病率和基于行为的 PA/SB干预，并充分阐明PA/SB剂量-反应健康关系。科学进步使 在这一领域，先进的分析和数据处理技术应用于可穿戴设备， 实验室校准研究的加速度计。科学上需要将校准研究从 固定持续时间的实验室模拟日常生活活动与自然观察的自由生活校准 以及加速度计算法训练和验证。这项提案的目的是填补这一重要的科学 知识差距。具体目标是：1）评估和改进机器学习算法来预测能源 24小时呼吸热量计停留期间的成本和活动类型; 2）验证机器学习加速度计 算法与现场派生，视频记录的直接观察;和3）验证机器学习算法 使用双标记水技术。我们高素质的研究团队将通过以下方式实现上述目标： 使用简短的可翻译功能测试将运动障碍人群分成健康， 上半身损伤、下半身损伤以及上半身和下半身损伤。最佳实践免费- 然后，将使用实时校准协议来训练、改进和评估功能集群特定的 用于预测活动能量成本、活动类型、活动转换和活动的加速度计算法 域这些拟议研究的结果将首次提供一个创新的和可翻译的 分类和评估残疾人和行动障碍者自由生活PA/SB的方法。