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
• 负责人: Scott E Crouter
• 金额: $ 64.08万
• 依托单位: UNIVERSITY OF TENNESSEE KNOXVILLE
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/10617774
• 关键词: Acceleration; Accelerometer; Adult; Algorithms; Ankle; Behavioral trial; Calorimetry; Classification; Clinical Trials; Collection; Complement; Data; Data Set; Development; Devices; Dose; Energy Metabolism; Future; Health; Hip region structure; Human; Indirect Calorimetry; Individual; Light; Linear Regressions; Location; Measures; Methods; Modeling; Moderate Activity; Moderate Exercise; Monitor; Motion; Movement; Outcome; Participant; Pattern; Performance; Physical activity; Physical assessment; Public Health; Research; Sampling; Source Code; Structure; Techniques; Technology; Testing; Time; Training; Work; Wrist; actigraphy; data repository; doubly-labeled water; health record; improved; machine learning algorithm; machine learning model; model building; model development; outcome prediction; physical conditioning; portability; response; sedentary; sedentary activity; sedentary lifestyle; sensor; software repository; standard measure; total energy expenditure; wearable device; wearable sensor technology

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.

项目摘要/摘要 可穿戴设备是客观评估体育活动（PA）类型和能量的主要方法 自由生活个人的款项（EE）。当前的实践涉及仅使用基于加速度计的设备， 通常可以更好地预测小组级别而不是个人级别的结果。天花板效果 已经达到了加速度计衍生预测的准确性和精度，因此存在关键 需要其他方法可以产生更准确和精确的方法来对PA类型进行分类和估计的其他方法 EE。潜在的解决方案是将加速度计的数据与其他传感器的数据相结合。加速度计 记录线性加速度，它捕获了大量的人类运动。但是，许多日常活动 包含仅使用加速度计捕获的转弯运动。陀螺仪记录角速度， 因此，与加速度计结合捕获人类运动的图像可能很有用。 评估PA类型和EE时，这可能会提高准确性和精度。使用Actigraph GT9X （戴在臀部，手腕或脚踝上），我们以前已经表明，加速度计和陀螺仪数据结合 仅与加速度计相比，个人级别的准确性提高了约6％。重要的是，这个 对久坐的活动进行分类，最多提高了30％。此外，分类的精度 仅使用加速度计时久坐不动的行为，范围为76.7-96.7％ 佩戴位置，而陀螺仪在所有穿着位置正确分类的时间为100％。总体 此R01应用的目的是使用EE的黄金标准措施（双标记的水，房间量热法 以及便携式间接量热法）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。研究的具体目的 是：1）开发和验证包括陀螺仪的机器学习模型对PA类型进行分类和估计 EE在成人中，使用24小时的间接量热法（n = 50）和2小时的半结构化活动使用24小时 便携式量热法（n = 50）; 2a）评估模型的自由生活表现，2B）重新培训并完善 使用直接观察和便携式间接热量法的自由生活数据的模型（n = 100） 在12小时的自由生活活动中的参与者）； 3）评估EE模型在延长的自由度期间的有效性 使用双标记的水技术（n = 100）的生活期。中心假设是陀螺仪将 提供有关人类运动过程中发生的旋转运动的有意义和歧视性信息， 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发生 与单独使用任何传感器相比 并将对评估成人自由生活PA的能力产生重大影响。