In this article, we propose WiSign that recognizes the continuous sentences of American Sign Language (ASL) with existing WiFi infrastructure. Instead of identifying the individual ASL words from the manually segmented ASL sentence in existing works, WiSign can automatically segment the original channel state information (CSI) based on the power spectral density (PSD) segmentation method. WiSign constructs a five-layer Deep Belief Network (DBN) to automatically extract the features of isolated fragments, and then uses the Hidden Markov Model (HMM) with Gaussian mixture and Forward-Backward algorithm to recognize sign words. In order to further improve the accuracy, WiSign also integrates the language model N-gram, which uses the grammar rules of ASL to calibrate the recognized results of sign words. We implement a prototype of WiSign with commercial WiFi devices and evaluate its performance in real indoor environments. The results show that WiSign achieves satisfactory accuracy when recognizing ASL sentences that involve the movements of the head, arms, hands, and fingers.

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在本文中，我们提出了使用现有 WiFi 基础设施识别美国手语 (ASL) 的连续句子的 WiSign。WiSign 可以根据功率谱密度（PSD）分割方法自动分割原始信道状态信息（CSI），而不是从现有作品中手动分割的 ASL 句子中识别单个 ASL 词。WiSign 构建了一个五层的深度信念网络（DBN）来自动提取孤立片段的特征，然后使用隐马尔可夫模型（HMM）和高斯混合和前向后向算法来识别符号词。为了进一步提高准确率，WiSign 还集成了语言模型 N-gram，它使用 ASL 的语法规则来校准手语的识别结果。我们使用商用 WiFi 设备实现 WiSign 原型，并评估其在真实室内环境中的性能。结果表明，在识别涉及头部、手臂、手和手指运动的 ASL 句子时，WiSign 取得了令人满意的准确性。