Mannini Andrea, Sabatini Angelo M, Intille Stephen S
The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
College of Computer and Information Science and Bouvé College of Health Sciences, Northeastern University, Boston, MA.
Pervasive Mob Comput. 2015 Aug 1;21:62-74. doi: 10.1016/j.pmcj.2015.06.003.
This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and wrist from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively.
这项工作描述了一种自动方法，可从原始加速度计数据中识别佩戴在身体五个不同部位（脚踝、大腿、臀部、手臂和手腕）的加速度计的位置。可穿戴传感器身体位置的自动检测将使系统能够让用户在不同身体部位灵活佩戴传感器，或者允许需要自动验证传感器放置位置的系统。这种两阶段位置检测算法的工作方式是，首先检测候选人行走的时间段（无论传感器位于何处）。然后，假设数据是关于行走的，该算法检测传感器的位置。使用留一法交叉验证方法，在一个比先前工作中使用的数据集大得多的数据集上对算法进行了验证。分别对97.4%和91.2%的分类数据窗口获得了正确的行走和放置识别结果。
