Suppr超能文献

使用带有重力消除身体活动分类算法的三轴加速度计区分儿童非运动和运动活动的预测模型。

Prediction models discriminating between nonlocomotive and locomotive activities in children using a triaxial accelerometer with a gravity-removal physical activity classification algorithm.

作者信息

Hikihara Yuki, Tanaka Chiaki, Oshima Yoshitake, Ohkawara Kazunori, Ishikawa-Takata Kazuko, Tanaka Shigeho

机构信息

Faculty of Engineering, Chiba Institute of Technology, Chiba, Japan.

Division of Integrated Sciences, J. F. Oberlin University, Tokyo, Japan.

出版信息

PLoS One. 2014 Apr 22;9(4):e94940. doi: 10.1371/journal.pone.0094940. eCollection 2014.

Abstract

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.

摘要

我们研究的目的是检验重力消除身体活动分类算法(GRPACA)是否适用于区分儿童各种身体活动(PA)中的非 locomotive 活动和 locomotive 活动,并证明这种方法能提高使用加速度计的儿童预测模型的估计准确性。邀请了就读于小学的日本儿童(42 名男孩和 26 名女孩)参与本研究。我们使用了采样间隔为 32 Hz 且测量范围为±6 G 的三轴加速度计。参与者被要求进行 6 种非 locomotive 活动和 5 种 locomotive 活动。我们用三轴加速度计测量原始合成加速度,并使用道格拉斯袋法监测每种活动期间的氧气消耗和二氧化碳产生。此外,在受试者坐在椅子上时测量静息代谢率(RMR)以计算代谢当量(METs)。当未过滤的合成加速度(USA)与过滤后的合成加速度(FSA)之比为 1.12 时,非 locomotive 活动和 locomotive 活动之间的正确区分率非常出色,平均为 99.1%。结果发现,除了上下攀爬外,非 locomotive 活动(METs = 0.013×合成加速度 + 1.220,R2 = 0.772)和 locomotive 活动(METs = 0.005×合成加速度 + 0.944,R2 = 0.880)均呈现出很强的线性关系。对于跑步、投球和拖地等中等至剧烈强度(>3.5 METs)的 PA,我们模型预测值与测量的 METs 之间的平均差异为 -0.50 至 0.23,这些被视为不可预测的 PA。此外,对于久坐至轻度中等强度(<3.5 METs)的 PA,差异在 0.25 METs 以内。我们用于区分儿童非 locomotive 活动和 locomotive 活动的特定校准模型,可用于评估非 locomotive 活动和 locomotive 活动从久坐到剧烈的 PA 强度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f380/3995680/e5b2e99d5af8/pone.0094940.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验