Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.
Sensors (Basel). 2018 Oct 24;18(11):3612. doi: 10.3390/s18113612.
Many motion sensor-based applications have been developed in recent years because they provide useful information about daily activities and current health status of users. However, most of these applications require knowledge of sensor positions. Therefore, this research focused on the problem of detecting sensor positions. We collected standing-still and walking sensor data at various body positions from ten subjects. The offset values were removed by subtracting the sensor data of standing-still phase from the walking data for each axis of each sensor unit. Our hierarchical classification technique is based on optimizing local classifiers. Many common features are computed, and informative features are selected for specific classifications. In this approach, local classifiers such as arm-side and hand-side discriminations yielded F1-scores of 0.99 and 1.00, correspondingly. Overall, the proposed method achieved an F1-score of 0.81 and 0.84 using accelerometers and gyroscopes, respectively. Furthermore, we also discuss contributive features and parameter tuning in this analysis.
近年来，已经开发出许多基于运动传感器的应用程序，因为它们提供了有关用户日常活动和当前健康状况的有用信息。然而，这些应用程序大多需要了解传感器的位置。因此，本研究专注于检测传感器位置的问题。我们从十个对象收集了在各种身体位置下的静止和行走传感器数据。对于每个传感器单元的每个轴，通过从行走数据中减去静止阶段的传感器数据来去除偏移值。我们的分层分类技术基于优化局部分类器。计算了许多常见特征，并为特定分类选择了信息量较大的特征。在这种方法中，臂侧和手侧的判别等局部分类器的 F1 分数分别为 0.99 和 1.00。总体而言，使用加速度计和陀螺仪，所提出的方法分别实现了 0.81 和 0.84 的 F1 分数。此外，我们还在该分析中讨论了有贡献的特征和参数调整。
