Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age.
Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age.
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DOI：
10.1088/0967-3334/36/11/2335
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发表时间：
2015-11
影响因子：
3.2
通讯作者：
Swartz AM
中科院分区：
文献类型：
--
作者：
Strath SJ;Kate RJ;Keenan KG;Welch WA;Swartz AM
为了开发和测试时间序列单站点和多站点放置模型，我们使用手腕、臀部和脚踝处理的加速度计数据来估计成人的能量成本和身体活动类型。三个年龄组（18-39 岁、40-64 岁、65 岁以上）的 99 名受试者在佩戴三个三轴加速度计时进行了 11 项活动：非优势手腕、臀部和脚踝各一个。在每次活动期间评估净氧消耗（MET）。加速度计信号的时间序列以称为箱的均匀离散值表示。支持向量机用于以箱和每对箱作为特征的活动分类。袋装决策树回归用于净代谢成本预测。为了评估模型性能，我们采用了折刀留一交叉验证方法。跨年龄组和年龄组内的单加速度计和多加速度计站点模型估计显示相似的准确性，偏差范围为-0.03至0.01 MET，偏差百分比为-0.8至0.3%，rMSE范围为0.81-1.04 MET。与单站点定位模型相比，多站点加速计定位模型改进了活动类型分类，准确度从最低的 69.3% 提高到最高 92.8%。对于每个加速计站点位置模型或组合站点位置模型，百分比准确度分类随着年龄组的函数而下降，或者当年轻年龄组模型推广到老年组时。特定年龄组模型的平均表现优于所有年龄组模型的组合。时间序列计算显示出预测能源成本和活动类型的有希望的结果。跨年龄组的预测存在差异，跨年龄组缺乏普遍性，以及特定年龄组的模型比所有年龄组合时的表现更好，这些都需要考虑作为进一步开发检测能源成本和类型的分析校准程序。
To develop and test time series single site and multi-site placement models, we used wrist, hip and ankle processed accelerometer data to estimate energy cost and type of physical activity in adults. Ninety-nine subjects in three age groups (18–39, 40–64, 65 + years) performed 11 activities while wearing three triaxial accelereometers: one each on the non-dominant wrist, hip, and ankle. During each activity net oxygen cost (METs) was assessed. The time series of accelerometer signals were represented in terms of uniformly discretized values called bins. Support Vector Machine was used for activity classification with bins and every pair of bins used as features. Bagged decision tree regression was used for net metabolic cost prediction. To evaluate model performance we employed the jackknife leave-one-out cross validation method. Single accelerometer and multi-accelerometer site model estimates across and within age group revealed similar accuracy, with a bias range of −0.03 to 0.01 METs, bias percent of −0.8 to 0.3%, and a rMSE range of 0.81–1.04 METs. Multi-site accelerometer location models improved activity type classification over single site location models from a low of 69.3% to a maximum of 92.8% accuracy. For each accelerometer site location model, or combined site location model, percent accuracy classification decreased as a function of age group, or when young age groups models were generalized to older age groups. Specific age group models on average performed better than when all age groups were combined. A time series computation show promising results for predicting energy cost and activity type. Differences in prediction across age group, a lack of generalizability across age groups, and that age group specific models perform better than when all ages are combined needs to be considered as analytic calibration procedures to detect energy cost and type are further developed.