A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor.

ABSTRACT: Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke' performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players' Forehand strokes r

SUBMITTER: Tabrizi SS 

PROVIDER: S-EPMC8033526 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature