具有视觉感知功能的下肢假肢嵌入式平台评价
Evaluation of Embedded Platforms for Lower Limb Prosthesis with Visual Sensing Capabilities
摘要：
下肢假肢可以从能够应用计算机视觉技术的嵌入式系统中受益，以增强自主控制和智能决策的上下文感知。为了填补目前文献中采用计算机视觉方法的假肢自主系统的空白，我们评估了两个现成平台的性能，Jetson TX2和Raspberry Pi 3，通过评估它们的CPU负载，内存使用，运行时间和分类精度不同的图像大小和广泛的计算机视觉算法。我们利用一个数据集，我们收集的地形识别使用安装在腿上的摄像头，这将使下肢假肢的上下文意识的图像。我们表明，合理的大图像和适当的帧选择方法，它是可以识别的地形，一个主题是要踩，重建表面和获得其倾斜的可能性。这是一个拟议的系统的一部分，配备了一个嵌入式相机和惯性测量单元，以识别不同类型的地形，并估计在受试者面前的表面的斜率。
英文摘要：
Lower limb prosthesis can benefit from embedded systems capable of applying computer vision techniques to enhance autonomous control and context awareness for intelligent decision making. In order to fill in the gap of current literature of autonomous systems for prosthetic legs employing computer vision methods, we evaluate the performance capabilities of two off-the-shelf platforms, the Jetson TX2 and Raspberry Pi 3, by assessing their CPU load, memory usage, run time and classification accuracy for different image sizes and widespread computer vision algorithms. We make use of a dataset that we collected for terrain recognition using images from a camera mounted on a leg, which would enable context-awareness for lower limb prosthesis. We show that, given reasonably large images and an appropriate frame selection method, it is possible to identify the terrain that a subject is going to step on, with the possibility of reconstructing the surface and obtaining its inclination. This is part of a proposed system equipped with an embedded camera and inertial measurement unit to recognize different types of terrain and estimate the slope of the surface in front of the subject.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
