A Feasibility Test on Preventing PRMDs Based on Deep Learning

  • So-Hyun Park Sookmyung Women’s University
  • Sun-Young Ihm Sookmyung Women’s University
  • Aziz Nasridinov Chungbuk National University
  • Young-Ho Park Sookmyung Women’s University

Abstract

This study proposes a method to reduce the playing-related musculoskeletal disorders (PRMDs) that often occur among pianists. Specifically, we propose a feasibility test that evaluates several state-of-the-art deep learning algorithms to prevent injuries of pianist. For this, we propose (1) a C3P dataset including various piano playing postures and show (2) the application of four learning algorithms, which demonstrated their superiority in video classification, to the proposed C3P datasets. To our knowledge, this is the first study that attempted to apply the deep learning paradigm to reduce the PRMDs in pianist. The experimental results demonstrated that the classification accuracy is 80% on average, indicating that the proposed hypothesis about the effectiveness of the deep learning algorithms to prevent injuries of pianist is true.

Published
2019-07-17
Section
Student Abstract Track