A wearable, self-calibrating, wireless sensor network for body motion processing

Kwang Yong Lim, F.Y.K. Goh, Wei Dong, K. Nguyen, I. Chen, S. Yeo, H. Duh, Chung Gon Kim
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引用次数: 36

Abstract

A novel self-calibrating sensing technology using miniature linear encoders and inertial/magnetic measurement unit (IMU) provides the accuracy, fast response and robustness required by many body motion processing applications. Our sensor unit consists of an accelerometer, a 3-axis magnetic sensor, 2 gyroscopes and a miniature linear encoder. The fusion of data from the sensors is accomplished by extracting the gravity related term from the accelerometer and consistently calibrating the gyroscopes and linear encoder when the sensor unit is under static conditions. Using the fused sensors, we developed a complete motion processing system that consists of a gateway where the human kinematics modeling is embedded. A time divided multiple access wireless architecture is adopted to synchronize the sensor network at 100 Hz. Experimental results show that the combination of the IMU and linear encoder produces a low RMS error of 3.5deg and correlation coefficient of 99.01%. A video showing the capture a performer's upper body motion is also realized.
一种可穿戴的、自校准的、用于身体运动处理的无线传感器网络
一种使用微型线性编码器和惯性/磁测量单元(IMU)的新型自校准传感技术提供了许多身体运动处理应用所需的精度,快速响应和鲁棒性。我们的传感器单元由一个加速度计、一个三轴磁传感器、两个陀螺仪和一个微型线性编码器组成。从加速度计中提取重力相关项,并在传感器单元处于静态状态时对陀螺仪和线性编码器进行持续校准,从而实现传感器数据的融合。利用融合传感器,我们开发了一个完整的运动处理系统,该系统由一个网关组成,其中嵌入了人体运动学建模。采用分时多址无线架构,在100hz频率下同步传感器网络。实验结果表明,IMU与线性编码器的组合产生了3.5°的低均方根误差和99.01%的相关系数。还实现了捕捉表演者上半身动作的视频。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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