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

项目摘要/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。 自由生活个体的自负性(EE)。目前的做法只涉及使用基于加速度计的设备，这 通常更适合在群体层面而不是个人层面预测结果。天花板效应 已经达到了加速度计派生预测的精确度和精确度，因此存在关键的 需要其他方法来产生更准确和精确的方法来对PA类型进行分类和估计 请看。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相结合。加速计 创纪录的线性加速度，捕捉到大量的人类运动。然而，许多日常活动 包含仅使用加速度计无法捕捉到的转弯运动。陀螺仪记录角速度， 因此与加速计结合可用于捕捉更丰富的人体运动图像。 这可以在评估PA类型和EE时提高准确度和精确度。使用活动记录仪GT9X (戴在臀部、手腕或脚踝上)，我们之前已经证明，结合加速度计和陀螺仪数据 与仅使用加速度计相比，个人级别的精度提高了约6%。重要的是，这件事- 包括对久坐活动分类的高达30%的改进。此外，分类准确率在 当只使用加速度计时，久坐和非久坐的行为从76.7%到96.7%不等 在所有磨损位置上，陀螺仪都能100%地正确识别。整体而言 此R01应用程序的目的是使用EE(双标记水、室内量热法)的金标准测量 和便携式间接量热计)和活动分类(视频直接观测)，以开发和完善 使用加速度计和陀螺仪传感器数据的中国学习算法。这项研究的具体目的 是：1)开发和验证包含陀螺仪的机器学习模型，对PA类型进行分类和估计 在成人中，使用24小时的房间停留间接量热法(n=50)和2小时的半结构化活动 便携式热量计(n=50)；2a)评估模型的自由生活性能，以及2b)重新培训和改进 使用来自直接观测和便携式间接量热计的地面真实数据的自由生活数据的模型(n=100 参与者在12小时的自由生活活动中)；以及3)在延长的自由生活活动期间评估EE模型的有效性 双标记水技术生存周期(n=100)。中心假设是陀螺仪将 提供有关人类运动期间发生的旋转运动的有意义和有区别的信息， 从而补充了加速度计数据。将加速度计和陀螺仪传感器数据结合在一起将有助于提高系统的性能。 与单独使用任何一个传感器相比，证明了分类PA类型和估计EE的准确性和精确度， 并将对评估成年人自由生活的PA的能力产生重大影响。

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.