Scholarlink

<p>Gesture interaction has emerged as a highly effective interface for intelligent human-computer interaction, attributed to its intuitive interaction modality and multidimensional control capabilities. However, traditional gesture interaction devices often depend on predefined encoding rules, which substantially limit interaction efficiency and degrade user experience. This study introduces an innovative intelligent finger ring interaction system based on a triboelectric nanogenerator utilizing PDMS/SrTiO<sub>3</sub> composite thin film (PS-TENG). The system maps freehand writing gestures directly to textual information input, thereby eliminating the need for complex gesture encoding schemes and offering a user-friendly, low-learning-curve input method. By integrating a deep learning model, the system achieves recognition accuracies of 98.21% for English letters, 96.87% for Arabic numerals, and 96.44% for Chinese characters. Furthermore, it supports secure and encrypted data transmission and enables wireless interaction for gaming control. These findings indicate that the intelligent finger ring interaction system possesses significant potential for practical applications in information input and wireless control.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Wearable interactive system with uncoded gesture recognition logic enabled by deep learning

  • 期刊论文

  • 发表日期：

    2025年10月14日

查看全文
  • Liangjie Liu,
  • Lingxiao Li,
  • Yibin Lin,
  • Wandi Chen,
  • Shuchen Weng,
  • Lei Sun,
  • Xiongtu Zhou,
  • Tailiang Guo,
  • Chaoxing Wu,
  • Yongai Zhang

摘要

Gesture interaction has emerged as a highly effective interface for intelligent human-computer interaction, attributed to its intuitive interaction modality and multidimensional control capabilities. However, traditional gesture interaction devices often depend on predefined encoding rules, which substantially limit interaction efficiency and degrade user experience. This study introduces an innovative intelligent finger ring interaction system based on a triboelectric nanogenerator utilizing PDMS/SrTiO3 composite thin film (PS-TENG). The system maps freehand writing gestures directly to textual information input, thereby eliminating the need for complex gesture encoding schemes and offering a user-friendly, low-learning-curve input method. By integrating a deep learning model, the system achieves recognition accuracies of 98.21% for English letters, 96.87% for Arabic numerals, and 96.44% for Chinese characters. Furthermore, it supports secure and encrypted data transmission and enables wireless interaction for gaming control. These findings indicate that the intelligent finger ring interaction system possesses significant potential for practical applications in information input and wireless control.