Department of Electronic Engineering, University of Rome "Tor Vergata", Via Politecnico 1, 00133 Rome, Italy.
Data Analysis Group, MathWorks, Matrix House, Cambridge Business Park, Cambridge CB4 0HH, UK.
Sensors (Basel). 2020 Jul 11;20(14):3879. doi: 10.3390/s20143879.
We propose a sign language recognition system based on wearable electronics and two different classification algorithms. The wearable electronics were made of a sensory glove and inertial measurement units to gather fingers, wrist, and arm/forearm movements. The classifiers were k-Nearest Neighbors with Dynamic Time Warping (that is a non-parametric method) and Convolutional Neural Networks (that is a parametric method). Ten sign-words were considered from the Italian Sign Language: cose, grazie, maestra, together with words with international meaning such as google, internet, jogging, pizza, television, twitter, and ciao. The signs were repeated one-hundred times each by seven people, five male and two females, aged 29-54 y ± 10.34 (SD). The adopted classifiers performed with an accuracy of 96.6% ± 3.4 (SD) for the k-Nearest Neighbors plus the Dynamic Time Warping and of 98.0% ± 2.0 (SD) for the Convolutional Neural Networks. Our system was made of wearable electronics among the most complete ones, and the classifiers top performed in comparison with other relevant works reported in the literature.
我们提出了一个基于可穿戴电子设备和两种不同分类算法的手语识别系统。可穿戴电子设备由感应手套和惯性测量单元组成，用于采集手指、手腕和手臂/前臂的运动。分类器是具有动态时间规整（即非参数方法）的 k 最近邻和卷积神经网络（即参数方法）。从意大利手语中考虑了十个手语词： cose、grazie、maestra，以及具有国际意义的单词，如 google、internet、jogging、pizza、television、twitter 和 ciao。这十个手语词由七个人重复一百次，其中五男两女，年龄 29-54 岁±10.34（SD）。采用的分类器对 k 最近邻加动态时间规整的准确率为 96.6%±3.4（SD），对卷积神经网络的准确率为 98.0%±2.0（SD）。我们的系统是最完整的可穿戴电子设备之一，与文献中报道的其他相关工作相比，分类器的表现也非常出色。
