Sign language is a crucial communication tool for the hearing impaired to interact with the outside world. However, the low prevalence of sign language leads to significant communication barriers between the hearing impaired and others. These barriers can be alleviated by using electronic sign language translation devices, but such devices typically face challenges related to their bulkiness, rigidity, and high cost. Herein, we present a cost-effective, flexible, and wearable device designed for the interpretation of sign language, leveraging machine learning algorithms to transform sign language movements into spoken language accurately. Our device is equipped with flexible and stretchable triboelectric sensor (FS-TS) arrays and a printed circuit board for efficient signal processing and wireless transmission. These FS-TSs are constructed from cost-effective materials including silicone, hydrogel, and fluorinated ethylene propylene (FEP) powders, which, while affordable, ensure rapid response and heightened sensitivity. By analyzing 1000 sets of sign language gestures with machine learning, our system has achieved an impressive recognition accuracy of 98.5 %. This achievement underscores the potential of our system as an economically viable and scalable solution for enhancing sign language recognition within the wearable electronics domain.

中文翻译：

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一种利用硅-水凝胶混合摩擦传感器阵列和机器学习的可穿戴手语翻译设备


手语是听障人士与外界互动的重要沟通工具。然而，手语的低流行率导致听力障碍者与他人之间存在重大沟通障碍。这些障碍可以通过使用电子手语翻译设备来缓解，但此类设备通常面临与其体积、刚性和高成本相关的挑战。在此，我们提出了一种经济高效、灵活且可穿戴的设备，专为手语解释而设计，利用机器学习算法将手语动作准确地转换为口语。我们的设备配备了灵活且可拉伸的摩擦电阻传感器 （FS-TS） 阵列和印刷电路板，可实现高效的信号处理和无线传输。这些 FS-TS 由具有成本效益的材料制成，包括硅胶、水凝胶和氟化乙烯丙烯 （FEP） 粉末，虽然价格实惠，但可确保快速响应和更高的灵敏度。通过机器学习分析 1000 组手语手势，我们的系统实现了令人印象深刻的 98.5% 的识别准确率。这一成就突显了我们的系统作为一种经济上可行且可扩展的解决方案的潜力，以增强可穿戴电子领域的手语识别能力。