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<p>Hand contact data, reflecting the intricate behaviours of human hands during object operation, exhibits significant potential for analysing hand operation patterns to guide the design of hand-related sensors and robots, and predicting object properties. However, these potential applications are hindered by the constraints of low resolution and incomplete capture of the hand contact data. Leveraging a non-contact and high-precision 3D scanning method for surface capture, a high-resolution and whole-body hand contact dataset, named as Ti3D-contact, is constructed in this work. The dataset, with an average resolution of 0.72 mm, contains 1872 sets of texture images and 3D models. The contact area during hand operation is whole-body painted on gloves, which are captured as the high-resolution original hand contact data through a 3D scanner. Reliability validation on Ti3D-contact is conducted and hand movement classification with 95% precision is achieved using the acquired hand contact dataset. The properties of high-resolution and whole-body capturing make the acquired dataset exhibit a promising potential application in hand posture recognition and hand movement prediction.</p>

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A high-resolution and whole-body dataset of hand-object contact areas based on 3D scanning method

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月18日

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  • Zelin Chen,
  • Hanlu Chen,
  • Yiming Ouyang,
  • Chenhao Cao,
  • Wei Gao,
  • Qiqiang Hu,
  • Hu Jin,
  • Shiwu Zhang

摘要

Hand contact data, reflecting the intricate behaviours of human hands during object operation, exhibits significant potential for analysing hand operation patterns to guide the design of hand-related sensors and robots, and predicting object properties. However, these potential applications are hindered by the constraints of low resolution and incomplete capture of the hand contact data. Leveraging a non-contact and high-precision 3D scanning method for surface capture, a high-resolution and whole-body hand contact dataset, named as Ti3D-contact, is constructed in this work. The dataset, with an average resolution of 0.72 mm, contains 1872 sets of texture images and 3D models. The contact area during hand operation is whole-body painted on gloves, which are captured as the high-resolution original hand contact data through a 3D scanner. Reliability validation on Ti3D-contact is conducted and hand movement classification with 95% precision is achieved using the acquired hand contact dataset. The properties of high-resolution and whole-body capturing make the acquired dataset exhibit a promising potential application in hand posture recognition and hand movement prediction.