Human action classification in basketball: A single inertial sensor based framework

Xiangyi Meng, Rui Xu, Xuantong Chen, Lingxiang Zheng, Ao Peng, Hai Lu, Haibin Shi, Biyu Tang, Huiru Zheng

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    Human Action Recognition is becoming more and more important in many fields, especially in sports. However, conventional algorithm are almost camera-based methods, which make it cumbersome and expensive. As the wearable inertial sensor has developed a lot, in this paper, we present a novel human action classification algorithm using in basketball, based on a single inertial sensor, which is a application of multi-label classification. We performed experiment on real world datasets. The AUPRC, AUROC and confusion matrix of our results demonstrated that our novel basketball motion recognizer have a great performance.

    Original languageEnglish
    Title of host publicationFrontier Computing - Theory, Technologies and Applications FC 2017
    PublisherSpringer
    Pages152-161
    Number of pages10
    Volume464
    ISBN (Print)9789811073977
    DOIs
    Publication statusPublished (in print/issue) - 19 Apr 2018
    Event6th International Conference on Frontier Computing, FC 2017 - Osaka, Japan
    Duration: 12 Jul 201714 Jul 2017

    Publication series

    NameLecture Notes in Electrical Engineering
    Volume464
    ISSN (Print)1876-1100
    ISSN (Electronic)1876-1119

    Conference

    Conference6th International Conference on Frontier Computing, FC 2017
    Country/TerritoryJapan
    CityOsaka
    Period12/07/1714/07/17

    Keywords

    • Basketball motion
    • Feature extraction
    • Human action recognition
    • Multi-label classification
    • Single inertial sensor
    • Support vector machine

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