About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
IoT 24(1):

Research Article

All-in-Net: Scorekeeping in Basketball Training using a Mobile Phone Camera and Image

Download73 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eetiot.9061,
        author={Yonit Rusho and Marcelo Sihman and Yeshayahu Huzler},
        title={All-in-Net: Scorekeeping in Basketball Training using a Mobile Phone Camera and Image},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2025},
        month={4},
        keywords={Mobile Computing Systems and Applications, Smart training systems, Image recognition},
        doi={10.4108/eetiot.9061}
    }
    
  • Yonit Rusho
    Marcelo Sihman
    Yeshayahu Huzler
    Year: 2025
    All-in-Net: Scorekeeping in Basketball Training using a Mobile Phone Camera and Image
    IOT
    EAI
    DOI: 10.4108/eetiot.9061
Yonit Rusho1,*, Marcelo Sihman1, Yeshayahu Huzler2
  • 1: Shenkar College of Engineering and Design
  • 2: Levinsky-Wingate Academic College
*Contact email: yonit@se.shenkar.ac.il

Abstract

As basketball is one of the crowd's favorite invasion games, methods are being developed to enhance players' performance as technology improves. Recording large parts of training significantly reduces subjective training assessment compared to objective aspects. In this paper, basketball players and coaches use a mobile application based on image processing for achievements, measuring shooting the basket from different positions on the court and displaying feedback. The shooting technique and the percentage of success are measured using an algorithm that identifies the angle of the shot to the basket. The system monitors players' knowledge of results during training using one mobile phone camera. The paper describes the architecture and design of the mobile computing and application: Pre-training (define goals based on past performance and level of training difficulty), during training (data collection and compatible camera), and post-training (analysis and visualization of results). Finally, the paper discusses validation and implications.

Keywords
Mobile Computing Systems and Applications, Smart training systems, Image recognition
Received
2025-04-11
Accepted
2025-04-11
Published
2025-04-11
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.9061
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL