Learning Sensor Interdependencies for IMU-to-Segment Assignment
Learning Sensor Interdependencies for IMU-to-Segment Assignment
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DOI：
10.1109/access.2021.3105801
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Tomoya Kaichi;Tsubasa Maruyama;M. Tada;H. Saito
中科院分区：
文献类型：
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作者：
Tomoya Kaichi;Tsubasa Maruyama;M. Tada;H. Saito
由于近年来惯性测量单元（imu）的技术进步，许多使用多个穿戴式惯性测量单元来测量人体运动的应用已经发展起来。在这些应用程序中，每个IMU必须附加到预定义的主体段。一种识别每个IMU所安装的身体部分的技术允许用户将惯性传感器附加到任意身体部分，这避免了由于传感器连接不正确而不得不重新测量。我们解决了这个单元到段的分配问题，并提出了一种新的端到端学习模型，该模型结合了全局特征生成模块和基于注意的机制。前者提取了所有附加imu的运动特征，后者使模型能够学习imu之间的依赖关系。因此，该模型基于全局运动和相关IMU的特征来确定IMU的位置。我们使用三种传感器配置的合成和真实公共数据集对所提出的方法进行了定量评估，其中包括安装15个传感器的全身配置。结果表明，对于所有数据集和传感器配置，我们的方法明显优于传统方法和基线方法。
Due to the recent technological advances in inertial measurement units (IMUs), many applications for the measurement of human motion using multiple body-worn IMUs have been developed. In these applications, each IMU has to be attached to a predefined body segment. A technique to identify the body segment on which each IMU is mounted allows users to attach inertial sensors to arbitrary body segments, which avoids having to remeasure due to incorrect attachment of the sensors. We address this IMU-to-segment assignment problem and propose a novel end-to-end learning model that incorporates a global feature generation module and an attention-based mechanism. The former extracts the feature representing the motion of all attached IMUs, and the latter enables the model to learn the dependency relationships between the IMUs. The proposed model thus identifies the IMU placement based on the features from global motion and relevant IMUs. We quantitatively evaluated the proposed method using synthetic and real public datasets with three sensor configurations, including a full-body configuration mounting 15 sensors. The results demonstrated that our approach significantly outperformed the conventional and baseline methods for all datasets and sensor configurations.