1The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, ITALY; 2College of Computer and Information Science and Bouvé College of Health Sciences, Northeastern University, Boston, MA; and 3Stanford Prevention Research Center, Stanford University, Stanford, CA.
Med Sci Sports Exerc. 2013 Nov;45(11):2193-203. doi: 10.1249/MSS.0b013e31829736d6.
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 purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities.
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-, 4-, 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 data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.
英国生物库和 NHANES 等大型身体活动监测项目正在使用基于腕戴加速度计的活动监测器来收集原始数据。其目的是通过让研究对象将监测器戴在手腕上而不是臀部上，从而增加佩戴时间，然后利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处理手腕和脚踝原始数据的算法，并将行为分类为四个广泛的活动类别：散步、骑车、久坐和其他活动。
佩戴腕戴和踝戴加速度计的参与者（N=33）进行了 26 项日常活动。采集、清理和预处理加速度计数据，以提取特征来描述 2、4 和 12.8 秒数据窗口。从原始信号分析中提取的运动频率和强度特征的特征向量被用于支持向量机分类器，以识别研究对象的活动。结果与人类观察者分类的类别进行了比较。使用留一法验证了算法。还评估了每个处理步骤的计算复杂度。
使用 12.8 秒的窗口，所提出的策略对踝部数据的分类准确率很高（95.0%），而腕部数据的准确率降低至 84.7%。更短的（4 秒）窗口仅使算法在腕部的性能略有下降至 84.2%。
使用 13 个特征的分类算法在原始数据集活动的复杂性下，可将数据很好地分类为四个类别。该算法计算效率高，在具有 4 秒延迟的移动设备上实时实现的计算复杂度也较低。
