Masters Matthew, Osborn Luke, Thakor Nitish, Soares Alcimar
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218.
Department of Electrical Engineering, Federal University of Uberlandia, Uberlandia, Brazil.
Int Conf Wearable Implant Body Sens Netw. 2015 Jun;2015. doi: 10.1109/bsn.2015.7299391. Epub 2015 Oct 19.
Limb tracking is an important aspect of human-machine interfaces (HMI). These systems, however, can often be limited by complex algorithms requiring significant processing power, obtrusive and immobile sensing techniques, and high costs. In this work, we utilize a sensor fusion algorithm implemented in commercial inertial measurement units (IMU) to combine accelerometer and gyroscope measurements in an effort to minimize computational requirements of the limb tracking system. In addition, previously developed methods were implemented to eliminate sensor drift by including information from a magnetometer. We tested the accuracy of our system by computing the root mean squared error (RMSE) of the true angle between the headings of two sensors and the estimate of that angle through quaternion-vector manipulations. An average RMSE of approximately 2.9° was achieved. Our limb tracking system is wearable, minimally complex, low-cost, and simple to use which has proven useful in multiple HMI applications discussed herein.
肢体跟踪是人机界面（HMI）的一个重要方面。然而，这些系统常常会受到复杂算法的限制，这些算法需要强大的处理能力、侵入性且固定不动的传感技术以及高昂的成本。在这项工作中，我们利用商业惯性测量单元（IMU）中实现的传感器融合算法，将加速度计和陀螺仪的测量数据相结合，以尽量减少肢体跟踪系统的计算需求。此外，通过纳入磁力计的信息，采用先前开发的方法来消除传感器漂移。我们通过计算两个传感器方向之间的真实角度与通过四元数 - 向量操作得到的该角度估计值之间的均方根误差（RMSE），来测试我们系统的准确性。平均RMSE约为2.9°。我们的肢体跟踪系统可穿戴、复杂度最低、成本低且使用简单，已证明在本文讨论的多个HMI应用中很有用。
