Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition,ACS Applied Materials & Interfaces

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Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2024-09-19 , DOI: 10.1021/acsami.4c10872
Hao-Yuan Shen _{1,

2} , Yu-Tao Li ₁ , Hang Liu ₃ , Jie Lin ₂ , Lu-Yu Zhao _{1,

2} , Guo-Peng Li ₂ , Yi-Wen Wu _{1,

2} , Tian-Ling Ren ₃ , Yeliang Wang ₁

Affiliation

Gesture sensors are essential to collect human movements for human–computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core–shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

中文翻译：

用石墨烯/碳纳米管制成的机器学习辅助手势传感器，用于手语识别

手势传感器对于收集人机界面的人体动作至关重要，但其应用通常因难以同时实现高灵敏度和超宽响应范围而受到阻碍。在本文中，受自然界蜘蛛丝结构的启发，提出了一种具有核壳结构的新型手势传感器。该传感器提供高达 340 的高应变系数和 60% 的宽响应范围。此外，传感器与深度学习技术相结合，创建了精确手势识别系统。该系统在单手势识别测试中表现出令人印象深刻的 99% 准确率。同时，利用滑动窗口技术和大语言模型，在连续句子识别方面实现了97%的准确率。总之，所提出的高性能传感器显着提高了手势识别传感器的灵敏度和响应范围。同时结合神经网络技术进一步改善手语使用者的日常交流方式。

更新日期：2024-09-19

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