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Prediction Models Discriminating between Nonlocomotive and Locomotive Activities in Children Using a Triaxial Accelerometer with a Gravity-removal Physical Activity Classification Algorithm

机译:具有重力去除运动分类算法的三轴加速度计识别儿童非机车和机车活动的预测模型

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摘要

The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220, R2 = 0.772) and locomotive (METs = 0.005×synthetic acceleration +0.944, R2 = 0.880) activities, except for climbing down and up. The mean differences between the values predicted by our model and measured METs were −0.50 to 0.23 for moderate to vigorous intensity (>3.5 METs) PAs like running, ball throwing and washing the floor, which were regarded as unpredictable PAs. In addition, the difference was within 0.25 METs for sedentary to mild moderate PAs (<3.5 METs). Our specific calibration model that discriminates between nonlocomotive and locomotive activities for children can be useful to evaluate the sedentary to vigorous PAs intensity of both nonlocomotive and locomotive activities.
机译:我们研究的目的是检验重力去除体力活动分类算法(GRPACA)是否适用于区分儿童的各种体力活动(PA)的非机车活动和机车活动,并证明这种方法提高了儿童的机体活动的估计准确性。使用加速度计的儿童预测模型。邀请上小学的日本儿童(42名男孩和26名女孩)参加这项研究。我们使用三轴加速度计,采样间隔为32 Hz,测量范围为±6G。要求参与者进行6次非机车活动和5次机车活动。我们使用三轴加速度计测量了原始的合成加速度,并使用道格拉斯袋法监测了每次活动中的氧气消耗和二氧化碳的产生。另外,在受试者坐在椅子上的情况下测量静息代谢率(RMR),以计算代谢当量(METs)。当未过滤的合成加速度(美国)和过滤的合成加速度(FSA)之比为1.12时,非机车和机车活动之间的正确判别率极高,平均为99.1%。结果,非机车(METs = 0.013×合成加速度+ 1.220,R 2 = 0.772)和机车(METs = 0.005×合成加速度+0.944,R 2 = 0.880)活动,但上下攀爬除外。通过我们的模型预测的值与测得的MET的平均差在-0.50到0.23之间,对于中等强度到剧烈强度(> 3.5 METs)的PA,例如奔跑,掷球和洗地板,这被认为是不可预测的PA。此外,久坐至轻度中度PA(<3.5 METs)的差异在0.25 METs之内。我们用于区分儿童的非机车活动和机车活动的特定校准模型对于评估非机车活动和机车活动的久坐到剧烈的PA强度很有用。

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