Recent advancements in wireless technologies enable pervasive and device free gesture recognition that enable assisted living utilizing off the shelf commercial Wi-Fi devices. This paper proposes a Device-Free Wi-Fi-based Sign Language Recognition (DF-WiSLR) for recognizing 30 static and 19 dynamic sign gestures. The raw Channel State Information (CSI) acquired from the Wi-Fi device for 49 sign gestures, with a volunteer performing the sign gestures in home and office environments. The proposed system adopts machine learning classifiers such as SVM, KNN, RF, NB, and a deep learning classifier CNN, for measuring the gesture recognition accuracy. To address the practical limitation of building a voluminous dataset, DF-WiSLR augments the originally acquired CSI values with Additive White Gaussian Noise (AWGN). Higher-order cumulant features of orders 2, 3, and 4 are extracted from the original and augmented data, as the machine learning classifiers demand manual feature extraction. To reduce the computational complexity of machine learning classifiers, an informative and reduced optimal feature subset is selected using MIFS. Whilst the pre-processed original and augmented CSI values directly fed as input to an 8-layer deep CNN, it performs auto feature extraction and selection. DF-WiSLR reported better recognition accuracies with SVM for static and dynamic gestures in both home and office environments. SVM achieved 93.4% 98.8% and 98.9% accuracies in home and office environments respectively, for static gestures. For dynamic gestures, 92.3% recognition accuracy achieved in home environment. On augmented data, the corresponding gesture recognition accuracy values reported are 97.1%, 99.9%, 99.9%, and 98.5%.

无线技术的最新进展实现了无处不在的无手势手势识别，可利用现成的商用Wi-Fi设备进行辅助生活。本文提出了一种基于设备的无Wi-Fi手势语言识别（DF-WiSLR），用于识别30种静态手势和19种动态手势。从Wi-Fi设备获取49种手势的原始信道状态信息（CSI），由志愿者在家庭和办公室环境中执行手势。提出的系统采用机器学习分类器（例如SVM，KNN，RF，NB和深度学习分类器CNN）来测量手势识别准确性。为了解决构建大量数据集的实际限制，DF-WiSLR使用加性高斯白噪声（AWGN）来增强最初获取的CSI值。由于机器学习分类器需要手动特征提取，因此从原始数据和增强数据中提取了2、3和4阶的高阶累积量特征。为了降低机器学习分类器的计算复杂性，使用MIFS选择了内容丰富且精简的最佳特征子集。将经过预处理的原始和增强CSI值直接作为输入馈送到8层深CNN时，它会执行自动特征提取和选择。DF-WiSLR报告说，在家庭和办公室环境中，SVM对于静态和动态手势都具有更好的识别精度。SVM在家庭和办公室环境中的静态手势准确率分别达到93.4％，98.8％和98.9％。对于动态手势，在家庭环境中可达到92.3％的识别精度。在扩充数据上