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首页> 外文会议>Mediterranean Conference on Medical and Biological Engineering and Computing >Automatic Identification of Sensor Localization on the Upper Extremity
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Automatic Identification of Sensor Localization on the Upper Extremity

机译：上肢上的传感器定位自动识别

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In this paper, we use 3D data from accelerome-ters and gyroscopes to identify the segment on which each inertial sensor is attached. An automatic procedure will reduce the time and skills needed to acquire motion capture data with inertial sensors, as well as reduce potential error. Our objective was to make a computationally low-cost algorithm that can be applied on a variety of models. We propose an algorithm based on features extracted from 3D accelerome-ter and gyroscope data. Our algorithm does not rely on training data or standard classifiers, and is based only on basic mathematical operations allowing for a fast (0.2s on average) and light procedure that is executable on nearly all platforms. We first tested our code on a wooden mockup of the upper extremity and subsequently applied it on data from three healthy subjects. Twelve features were selected for analysis on four different movements. Each motion was performed at three different speeds to assess the potential use of the algorithm on patient populations. The results indicate that this algorithm can be used to identify the segments on which the inertial sensors are located. The only information required prior to execution of the algorithm is the number of segments that are involved in the model.
机译：在本文中，我们使用来自加速器和陀螺仪的3D数据来识别所连接每个惯性传感器的段。自动过程将减少使用惯性传感器获取运动捕捉数据所需的时间和技能，以及减少潜在误差。我们的目标是进行计算上的低成本算法，可以应用于各种型号。我们提出了一种基于从3D加速器和陀螺数据提取的特征的算法。我们的算法不依赖于培训数据或标准分类器，并且仅基于基本数学操作，允许在几乎所有平台上可执行的快速（平均平均值0.2s）和光程序。我们首先在上肢的木制样机上测试了我们的代码，随后将其应用于来自三个健康科目的数据。选择了十二个特征以分析四种不同的运动。每个运动以三种不同的速度进行，以评估算法对患者群体的潜在使用。结果表明该算法可用于识别惯性传感器所在的段。执行算法之前所需的唯一信息是模型中涉及的段数。

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