Meriem Mhedhbi, M. Laaraiedh, B. Uguen
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引用次数: 13

摘要

体域网络是一个新兴的领域,引起了开发人员和系统设计人员的极大兴趣。另一方面,在不同的应用程序中,本地化的需求变得越来越必要。在此背景下,本文的目的是估计人体的不同手势和运动。最初,我们使用从C3D文件中提取的关于人体运动的信息。事实上,这些文件为我们提供了一个移动物体上传感器的精确3D坐标。第二步,采用IEEE 802.15.6信道模型估计传感器之间的距离,这些传感器是基于多维尺度的运动技术的输入。基本上,这种技术并没有呈现出令人满意的结果,这就是为什么我们通过SVD重建算法和添加距离约束来改进我们的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constrained LMDS technique for human motion and gesture estimation
Body Area Networks is an emerging domain taking a big interest from developers and system designers. On the other hand, the need to localize is becoming necessary in diverse applications. Within this context, the aim of this paper is to estimate the different gestures and motions of the human body. Initially, we use information, about human motion, extracted from C3D files. In fact, these files provide us with the exact 3D coordinates of the sensors on a moving body. In a second step the IEEE 802.15.6 channel model is used to estimate the distances between sensors which are the input of the locomotion technique based on Multidimensional Scaling. Basically, this technique did not present satisfying results, that's why we have improved our results by an SVD reconstruction algorithm and by adding distance constraints.
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