Scholarlink

<p>A vast population of visually impaired individuals is currently facing intricate life challenges, particularly related to perceiving walking directions. Therefore, this paper proposes a novel deep learning method based on wearable sensors to address the problem of walking direction recognition. The information mining and fusion module, the multi-feature position information mining attention module, and the multi-feature content information mining attention module are proposed to comprehensively mine comprehensive information from walking data. To overcome the limitation of information gathered from a single type of sensor, this paper combines inertial sensors and pressure insoles for walking direction recognition. Experimental results demonstrate that compared to existing research methods, the proposed method in this paper achieves a higher recognition accuracy highlighting the superiority and effectiveness of this method.</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

Walking direction recognition based on deep learning with inertial sensors and pressure insoles

  • 期刊论文

  • 发表日期：

    2025年1月4日

查看全文
  • Ming Guo,
  • Lipeng Qin,
  • Mengxue Yan,
  • Kun Zhou,
  • Xiangyong Chen,
  • Jianlong Qiu

摘要

A vast population of visually impaired individuals is currently facing intricate life challenges, particularly related to perceiving walking directions. Therefore, this paper proposes a novel deep learning method based on wearable sensors to address the problem of walking direction recognition. The information mining and fusion module, the multi-feature position information mining attention module, and the multi-feature content information mining attention module are proposed to comprehensively mine comprehensive information from walking data. To overcome the limitation of information gathered from a single type of sensor, this paper combines inertial sensors and pressure insoles for walking direction recognition. Experimental results demonstrate that compared to existing research methods, the proposed method in this paper achieves a higher recognition accuracy highlighting the superiority and effectiveness of this method.