Activity recognition using a single accelerometer placed at the wrist or ankle.
Activity recognition using a single accelerometer placed at the wrist or ankle.
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DOI：
10.1249/mss.0b013e31829736d6
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发表时间：
2013-11
影响因子：
4.1
通讯作者：
Haskell W
中科院分区：
文献类型：
--
作者：
Mannini A;Intille SS;Rosenberger M;Sabatini AM;Haskell W
大型身体活动监测项目，如英国生物银行和NHANES，正在使用腕戴式基于加速度计的活动监测器来收集原始数据。目标是通过要求受试者将监测器佩戴在手腕而不是臀部来增加佩戴时间，然后使用原始信号中的信息来改善活动类型和强度估计。这项工作的目的是获得一种算法来处理手腕和脚踝的原始数据，并将行为分为四个广泛的活动类别：步行，骑自行车，久坐和其他。参与者（N = 33）在手腕和脚踝上佩戴加速度计进行26次日常活动。收集、清理和预处理加速度计数据，以提取表征2 s、4 s和12.8 s数据窗口的特征。从原始信号的分析中提取的运动的频率和强度的编码信息的特征向量与支持向量机分类器一起用于识别受试者的活动。将结果与人类观察者分类的类别进行比较。算法进行了验证，使用留一个主题的策略。还评估了每个处理步骤的计算复杂性。在12.8 s的窗口下，所提出的策略对踝关节数据显示出高的分类准确率（95.0%），而对手腕数据则降低到84.7%。较短（4 s）窗口仅将手腕上算法的性能最低限度地降低至84.2%。一个使用13个特征的分类算法显示出很好的分类到四个类中，给出了原始数据集中活动的复杂性。该算法计算效率高，可以在移动的设备上实时实现，只有4秒的延迟。
Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purpose of this work is obtaining an algorithm to process wrist and ankle raw data and classify behavior into four broad activity classes: ambulation, cycling, sedentary and other. Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2 s, 4 s, and 12.8 s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject’s activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. With 12.8 s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original dataset. The algorithm is computationally-efficient and could be implemented in real-time on mobile devices with only 4 s latency.