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    HappyFeet: Challenges in Building an Automated Dance Recognition and Assessment Tool

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    GetMobile2018_Prepublished_Final_Draft.pdf (789.1Kb)
    Links to Files
    https://dl.acm.org/citation.cfm?id=3308759
    Permanent Link
    10.1145/3308755.3308759
    http://hdl.handle.net/11603/12774
    Collections
    • UMBC Faculty Collection
    • UMBC Information Systems Department
    • UMBC Student Collection
    Metadata
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    Author/Creator
    Faridee, Abu Zaher Md
    Ramamurthy, Sreenivasan Ramasamy
    Roy, Nirmalya
    Date
    2018-09
    Type of Work
    7 pages
    Text
    journal articles postprints
    Citation of Original Publication
    Abu Zaher Md Faridee ,Sreenivasan Ramasamy Ramamurthy, Nirmalya Roy, HappyFeet: Challenges in Building an Automated Dance Recognition and Assessment Tool, GetMobile: Mobile Computing and Communications archive Volume 22 Issue 3, September 2018 Pages 10-16 , DOI: 10.1145/3308755.3308759
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    IoT devices
    happyfeet
    dance activity recognition(DAR)
    body sensors
    energy profiles
    Abstract
    In this paper, we discuss our experience in building an automated dance assessment tool with IMU and IoT devices and highlight the major challenges of such an endeavor. In a typical dance classroom scenario, where the students frequently outnumber their instructors, such a system can add an immense value to both parties by providing systematic breakdown of the dance moves, comparing the dance moves between the students and the instructors, and pinpointing the places for improvement in an autonomous way. Along that direction, our prototypical work, HappyFeet [1], showcases our initial attempts of developing such an intelligent Dance Activity Recognition (DAR) system. Our CNN based Body Sensor Network proves more effective (by ≈7% margin at 94.20%) at accurately recognizing the micro-steps of the dance activities than traditional feature engineering approaches. These metrics are derived by purposely evaluating the setup on a dance form known for its gentle, smooth and subtle limb movements. In this paper, we articulate how our proposed DAR framework will be generalizable for diverse dance styles involving very pronounced movements, human body kinematics and energy profiles.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.