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 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.

中文翻译：

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使用IMU传感器的乒乓球正手击球评估系统的深度学习方法

心理和行为证据表明，在COVID-19锁定期间，家庭体育活动可减少负面情绪和焦虑。低成本，非侵入式且具有隐私保护功能的智能虚拟教练乒乓球训练协助可以帮助保持在家中的活跃和健康。在本文中，进行了一项研究，以开发正手击球性能评估系统，将其作为虚拟教练乒乓球皮影训练系统的第二个主要组成部分。进行这项研究是为了证明与2DCNN和RBF-SVR时间序列分析和机器学习方法相比，所提出的LSTM模型在评估作者提供的乒乓球正手正手皮影比赛感官数据方面的有效性。生成的数据包括16个运动员的正手球拍的运动和方向测量；除了，笔画的评估分数由三位教练分配。作者研究了由超参数值改变的ML模型的行为。RMSE加权平均值的实验结果表明，改进的LSTM模型比2DCNN和RBF-SVR分别实现了33.79％和4.24％的估计误差。然而结果表明，所有非线性回归模型都足够适合观察到的数据。修改后的LSTM是当前研究中所有三种正手技术中最强大的回归方法。