A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor.
Comput Intell Neurosci
; 2021: 5584756, 2021.
Article
in English
| MEDLINE | ID: covidwho-1177603
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 racket's movement and orientation measurements; besides, the strokes' evaluation scores were assigned by the three coaches. The authors investigated the ML models' behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Tennis
/
Deep Learning
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Comput Intell Neurosci
Journal subject:
Medical Informatics
/
Neurology
Year:
2021
Document Type:
Article
Affiliation country:
2021
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