Atzori Manfredo, Gijsberts Arjan, Kuzborskij Ilja, Elsig Simone, Hager Anne-Gabrielle Mittaz, Deriaz Olivier, Castellini Claudio, Muller Henning, Caputo Barbara
IEEE Trans Neural Syst Rehabil Eng. 2015 Jan;23(1):73-83. doi: 10.1109/TNSRE.2014.2328495. Epub 2014 Jun 4.
In this paper, we characterize the Ninapro database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by jointly recording surface electromyography signals from the forearm and kinematics of the hand and wrist while subjects perform a predefined set of actions and postures. Besides describing the acquisition protocol, overall features of the datasets and the processing procedures in detail, we present benchmark classification results using a variety of feature representations and classifiers. Our comparison shows that simple feature representations such as mean absolute value and waveform length can achieve similar performance to the computationally more demanding marginal discrete wavelet transform. With respect to classification methods, the nonlinear support vector machine was found to be the only method consistently achieving high performance regardless of the type of feature representation. Furthermore, statistical analysis of these results shows that classification accuracy is negatively correlated with the subject's Body Mass Index. The analysis and the results described in this paper aim to be a strong baseline for the Ninapro database. Thanks to the Ninapro database (and the characterization described in this paper), the scientific community has the opportunity to converge to a common position on hand movement recognition by surface electromyography, a field capable to strongly affect hand prosthesis capabilities.
在本文中，我们对Ninapro数据库进行了特性描述，并阐述了其作为手部假肢评估基准的用途。该数据库是一个公开可用的资源，旨在支持先进肌电手部假肢的研究。该数据库是通过在受试者执行一组预定义的动作和姿势时，同步记录来自前臂的表面肌电信号以及手部和腕部的运动学数据而获得的。除了详细描述采集协议、数据集的整体特征和处理程序外，我们还使用各种特征表示和分类器展示了基准分类结果。我们的比较表明，简单的特征表示（如均值绝对值和波形长度）能够实现与计算要求更高的边际离散小波变换相似的性能。关于分类方法，发现非线性支持向量机是唯一一种无论特征表示类型如何都能始终实现高性能的方法。此外，对这些结果的统计分析表明，分类准确率与受试者的体重指数呈负相关。本文所描述的分析和结果旨在成为Ninapro数据库的一个强大基线。得益于Ninapro数据库（以及本文所描述的特性），科学界有机会在通过表面肌电进行手部运动识别这一领域达成共识，该领域能够对假肢功能产生重大影响。
