使用放置在躯干上的imu模拟动态身体运动

Alanna Vial, D. Stirling, M. Ros, P. Vial, M. Field
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引用次数: 1

摘要

本文研究了符号聚合近似(SAX)在单惯性测量单元(IMU)下动态物体运动建模中的应用。此外,该研究还展示了位于身体周围不同位置的imu如何产生可比较的结果。本研究探讨了用于监测运动的多个IMU传感器的输出。接下来,通过分析测量的旋转和位置IMU数据,对胸骨、骨盆、头部和下背部的传感器位置进行比较。此外,通过增加训练数据中的信息来改进分类器,以避免对相似活动的错误分类。本研究的结果也证明,胸骨和头部传感器在使用tsb进行分类时,特别是在用于动态活动分类时,提供了与骨盆传感器相当的数据。为了对数据进行预处理,采用子维motif发现方法在多个imu的数据中寻找特征。这改进了先前的研究,这些研究表明使用胸骨IMU很难对快速运动进行分类。该数据也使用SAX进行近似,并通过比较时间序列位图(TSB’s)进行分类,以找到参考TSB’s和滑动窗口TSB’s之间的最小欧几里得距离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling dynamic body motion using IMUs positioned on the torso
This study investigated the application of Symbolic Aggregate approXimation (SAX) to modelling dynamic body motion using a single inertial measurement unit (IMU). In addition this study demonstrates how IMUs located at different positions around the body produce comparable results. This study investigates the output of multiple IMU sensors, employed to monitor movement. Next a comparison of the sternum, pelvis, head and lower back sensor locations is conducted by analysing the measured rotation and position IMU data. Additionally, the classifier has been improved by increasing the information in the training data to avoid incorrect classification of similar activities. The results obtained in this study also prove that the sternum and head sensors provided comparable data to the pelvis sensor when using TSBs for classification, especially when used to classify dynamic activities. To pre-process the data, sub-dimensional motif discovery was employed to find features within the data from multiple IMUs. This improves on previous studies which illustrated difficulty classifying fast movements using the sternum IMU. This data was also approximated using SAX and classified by comparing Time Series Bitmaps (TSB's) to find the least Euclidean distance between the reference TSB's and the sliding window TSB's.
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