Use of accelerometer and gyroscope data to improve precision of estimates of physical activity type and energy expenditure in free-living adults

使用加速度计和陀螺仪数据来提高自由生活成年人身体活动类型和能量消耗的估计精度

基本信息

  • 批准号:
    10444075
  • 负责人:
  • 金额:
    $ 68.45万
  • 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:
    2022-05-15 至 2026-04-30
  • 项目状态:
    未结题

项目摘要

Project Summary/Abstract Wearable devices are the primary method for objectively assessing physical activity (PA) type and energy ex- penditure (EE) in free-living individuals. Current practice involves using only accelerometer-based devices, which are generally better for predicting outcomes at the group level rather than the individual level. A ceiling effect has been reached for accuracy and precision of accelerometer-derived predictions, and thus there is a critical need for other approaches that can yield more accurate and precise methods to classify PA type and estimate EE. A potential solution is to combine data from accelerometers with data from other sensors. Accelerometers record linear acceleration, which captures a large amount of human movement. However, many daily activities contain turning motions that are not captured by only using accelerometers. Gyroscopes record angular velocity, and thus may be useful in combination with accelerometers for capturing a richer picture of human movement. This can result in improved accuracy and precision when assessing PA type and EE. Using an ActiGraph GT9X (worn on hip, wrists, or ankles), we have previously shown that combining accelerometer and gyroscope data led to individual-level accuracy improvements of ~6%, compared to accelerometer only. Importantly, this in- cluded up to 30% improvement for classifying sedentary activities. In addition, classification accuracy between sedentary and non-sedentary behaviors when using only the accelerometer, ranged from 76.7-96.7% across wear locations, whereas the gyroscope correctly classified 100% of the time at all wear locations. The overall objective of this R01 application is to use gold standard measures of EE (doubly-labeled water, room calorimetry and portable indirect calorimetry) and activity classification (video direct observation) to develop and refine ma- chine learning algorithms using both accelerometer and gyroscope sensor data. The specific aims of the study are: 1) Develop and validate gyroscope-inclusive machine learning models that classify PA type and estimate EE in adults, using a 24-hr stay in a room indirect calorimetry (n=50) and 2-hr of semi-structured activities with portable calorimetry (n=50); 2a) Assess free-living performance of the models, and 2b) Re-train and refine the models using free-living data with ground truth from direct observation and portable indirect calorimetry (n = 100 participants during 12 hrs of free-living activity); and 3) Assess validity of EE models during a prolonged free- living period using the doubly-labeled water technique (n=100). The central hypothesis is that the gyroscope will provide meaningful and discriminative information on rotational movements that occur during human movement, thereby complementing the accelerometer data. Combining accelerometer and gyroscope sensor data will im- prove accuracy and precision for classifying PA type and estimating EE compared to using either sensor alone, and will have a significant impact on the ability to assess free-living PA in adults.
项目总结/摘要 可穿戴设备是客观评估身体活动(PA)类型和能量消耗的主要方法。 自由生活的个人的支出(EE)。目前的做法只涉及使用基于加速度计的设备, 一般来说,在群体水平上预测结果比在个人水平上更好。上限效应 已经达到的准确性和精度的加速度计衍生的预测,因此,有一个关键的 需要其他方法,可以产生更准确和精确的方法来分类PA类型和估计 EE.一个潜在的解决方案是将来自加速度计的数据与来自其他传感器的数据进行联合收割机组合。加速计 记录线性加速度,捕捉大量的人体运动。许多日常活动 包含仅使用加速度计无法捕获的转动运动。陀螺仪记录角速度, 因此可以与加速度计结合使用,以捕获更丰富的人体运动图像。 这可以提高评估PA类型和EE时的准确度和精密度。使用ActiGraph GT 9 X (worn在臀部,手腕,或脚踝),我们以前已经表明,结合加速度计和陀螺仪数据, 与仅加速度计相比,单个级精度提高约6%。重要的是,这在- 包括对久坐活动分类的改善达30%。此外,分类精度在 久坐和非久坐行为时,只使用加速度计,范围从76.7-96.7%, 磨损位置,而陀螺仪正确分类100%的时间在所有磨损位置。整体 本R 01应用的目的是使用EE的金标准测量(双标记水,室内量热法 和便携式间接量热法)和活动分类(视频直接观察),以开发和完善ma- 使用加速度计和陀螺仪传感器数据的chine学习算法。研究的具体目标 1)开发和验证陀螺仪包含的机器学习模型,该模型对PA类型进行分类并估计 成人EE,使用24小时室内间接测热法(n=50)和2小时半结构化活动, 便携式量热仪(n=50); 2a)评估模型的自由生活表现,以及2b)重新训练和完善 模型使用自由生活的数据与地面实况直接观察和便携式间接量热法(n = 100 参与者在12小时的自由生活活动期间);和3)评估EE模型在长期自由生活期间的有效性 使用双标记水技术的存活期(n=100)。核心假设是,陀螺仪将 提供关于在人体运动期间发生的旋转运动的有意义的和有区别的信息, 从而补充加速度计数据。结合加速度计和陀螺仪传感器数据将提高- 与单独使用任一传感器相比,证明了PA类型分类和EE估计的准确性和精确性, 并将对评估成人自由生活PA的能力产生重大影响。

项目成果

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Scott E Crouter其他文献

Scott E Crouter的其他文献

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{{ truncateString('Scott E Crouter', 18)}}的其他基金

Use of accelerometer and gyroscope data to improve precision of estimates of physical activity type and energy expenditure in free-living adults
使用加速度计和陀螺仪数据来提高自由生活成年人身体活动类型和能量消耗的估计精度
  • 批准号:
    10617774
  • 财政年份:
    2022
  • 资助金额:
    $ 68.45万
  • 项目类别:
Novel Approaches for Predicting Unstructured Short Periods of Physical Activities in Youth
预测青少年非结构化短期体育活动的新方法
  • 批准号:
    9030093
  • 财政年份:
    2016
  • 资助金额:
    $ 68.45万
  • 项目类别:
Novel Techniques for the Assessment of Physical Activity in Children
评估儿童身体活动的新技术
  • 批准号:
    7661581
  • 财政年份:
    2009
  • 资助金额:
    $ 68.45万
  • 项目类别:
Novel Techniques for the Assessment of Physical Activity in Children
评估儿童身体活动的新技术
  • 批准号:
    7869361
  • 财政年份:
    2009
  • 资助金额:
    $ 68.45万
  • 项目类别:

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Use of accelerometer and gyroscope data to improve precision of estimates of physical activity type and energy expenditure in free-living adults
使用加速度计和陀螺仪数据来提高自由生活成年人身体活动类型和能量消耗的估计精度
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