Control buggy using Leap sensor camera in Data Mining Domain

  • Unique Paper ID: 146526
  • Volume: 4
  • Issue: 12
  • PageNo: 855-860
  • Abstract:
  • This paper explores a new challenge: modeling the devices or framework semi-automatically. This framework or gadget work utilizing gestures. We control system utilizing distinctive gestures. The hand movement data is caught utilizing leap sensor motion camera. To identify appropriate motion we separate diverse features of different gesture from hand motion data. For extracting highlights we utilize eucludial distance formula and for comparision we use cosine similarity equation. We likewise have an elective method for correlation which is KNN. In the past framework 3D camera was utilized so they had less exactness. In this paper we have extracted more features so this framework is more exact than the past one. This framework diminishes human endeavors and it can lessen mischances to a more noteworthy broaden. Such framework would make driving simple and agreeable.
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Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{146526,
        author = {Kurup Amitha  and Jadhav Snehal  and Makar Shraddha and Ghule Priyanka},
        title = {Control buggy using Leap sensor camera in Data Mining Domain},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {12},
        pages = {855-860},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146526},
        abstract = {This paper explores a new challenge: modeling the devices or framework semi-automatically. This framework or gadget work utilizing gestures. We control system utilizing distinctive gestures. The hand movement data is caught utilizing leap sensor motion camera. To identify appropriate motion we separate diverse features of different gesture from hand motion data. For extracting highlights we utilize eucludial distance formula and for comparision we use cosine similarity equation. We likewise have an elective method for correlation which is KNN. 
In the past framework 3D camera was utilized so they had less exactness. In this paper we have extracted more features so this framework is more exact than the past one. This framework diminishes human endeavors and it can lessen mischances to a more noteworthy broaden. Such framework would make driving simple and agreeable.
},
        keywords = {Leap motion, gesture, Euclidean distance, cosine similarity, Trajectory feature, gesture recognition },
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 4
  • Issue: 12
  • PageNo: 855-860

Control buggy using Leap sensor camera in Data Mining Domain

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