Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle.
Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle.
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DOI：
10.1249/mss.0000000000001144
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发表时间：
2017-04
影响因子：
4.1
通讯作者：
Intille SS
中科院分区：
文献类型：
--
作者：
Mannini A;Rosenberger M;Haskell WL;Sabatini AM;Intille SS
使用来自身体佩戴的加速度计的原始数据来识别人类活动的最先进的方法主要已经用从成年人收集的数据进行了验证。本研究应用了一种以前可用的方法，使用手腕或脚踝加速度计对从成人和青年收集的数据集进行活动分类。一种用于检测腕戴式加速度计活动的算法，最初使用33名成年人的数据开发，在20名青年（年龄13±1.3）的数据集上进行测试。该算法还通过添加所需的新功能来扩展，以提高青年数据集的性能。随后对成人和青年数据进行了交叉测试（对一组进行培训，对另一组进行测试）和留一个受试者交叉验证。新功能集使用手腕数据将成年人的整体识别率提高了2.3%，年轻人提高了5.1%。留一主题交叉验证准确率性能为87.0%（手腕）和94.8%（脚踝）的成年人，91.0%（手腕）和92.4%（脚踝）的青年。合并两个数据集，总体准确率为88.5%（手腕）和91.6%（脚踝）。以前可用的成人活动分类方法可以推广到青年数据。将青年数据纳入培训阶段，并使用旨在收集青年参与者特殊活动分散信息的功能，可以使方法框架更好地适应青年活动的特点，提高其总体绩效。所提出的算法区分ammonia从久坐不动的活动，涉及手势的手腕数据，如正在收集的大型监测研究。
State-of-the-art methods for recognizing human activity using raw data from body worn accelerometers have primarily been validated with data collected from adults. This study applies a previously available method for activity classification using wrist or ankle accelerometer to work on datasets collected from both adults and youth. An algorithm for detecting activity from wrist-worn accelerometers, originally developed using data from 33 adults, is tested on a dataset of 20 youth (age 13±1.3). The algorithm is also extended by adding new features required to improve performance on the youth dataset. Subsequent tests on both the adult and youth data were performed using crossed tests (training on one group and testing on the other) and leave-one-subject-out cross-validation. The new feature set improved overall recognition using wrist data by 2.3% for adults and 5.1% for youth. Leave-one-subject-out cross-validation accuracy performance was 87.0% (wrist) and 94.8% (ankle) for adults, and 91.0% (wrist) and 92.4% (ankle) for youth. Merging the two datasets, overall accuracy was 88.5% (wrist) and 91.6% (ankle). Previously available methodological approaches for activity classification in adults can be extended to youth data. Including youth data in the training phase and using features designed to capture information on the peculiar activity fragmentation of young participants allows a better fit of the methodological framework to the characteristics of activity in youth, improving its overall performance. The proposed algorithm differentiates ambulation from sedentary activities that involve gesturing in wrist data, such as that being collected in large surveillance studies.