基于Myo臂带和Leap运动的全臂跟踪传感器数据融合

Eider C. P. Silva, E. Clua, A. Montenegro
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引用次数: 15

摘要

Myo臂带(Myo)是一种可以安装在前臂上的传感器,它可以将手臂运动的肌肉模式发送到配备蓝牙的设备上,这些肌肉模式是由手部的一些手势触发的。此外,它还能根据内置的陀螺仪、加速度计和磁力计产生旋转数据。Leap Motion (LM)是一个基于传感器的摄像头和红外灯,可以准确地跟踪手的运动,包括手指。因此,这项工作描述了传感器数据融合技术的使用,将来自两个传感器的数据结合起来,以便创建手臂运动的实时3D虚拟模拟,包括前臂,手和手指。这种组合的目的也是为了提高性能,旨在克服主要传感器单独使用时的局限性,就像Myo一样,它仅限于产生前臂运动数据,而LM则具有有限的视野和范围跟踪。实验结果表明,所研究的算法能够将两个传感器的数据结合起来,在机械臂的精度和跟踪性方面有较大的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensor Data Fusion for Full Arm Tracking Using Myo Armband and Leap Motion
The Myo Armband (Myo) is a sensor which can be attached to the forearm, and allows to send to devices equipped with Bluetooth muscular patterns of arm movements triggered by some gestures made by the hand. Also, it produces rotation data based on a gyroscope, accelerometer and magnetometer embedded on it. The Leap Motion (LM) is a sensor-based cameras and infrared lights, which allows accurately to track the hands motion, including fingers. Therefore, this work describes the use of sensor data fusion techniques to combine the data from both sensors in order to create a 3D virtual simulation in real-time of an arm motion, including the forearm, hand and fingers. The purpose of this combination is also improve the performance, aiming to overcome the main sensors' limitations when they are used individually, as occurs with Myo, which is limited to produce only forearm motion data and the LM, which has a limited field of view and range tracking. The experiment results shows that the studied algorithm is able to combines the data from both sensors achieving a considerable gain in the precision and tracking of the arm.
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