Tachycardia as "Shadow Play"

Reconstruction of somatosensory interaction in Fuzhou Shadow Play based on depth image recognition

10.1117/12.2624856 ◽

2021 ◽

Author(s):

Peng Shan ◽

Wenzhi Wang ◽

Wenxuan Zhao ◽

Zishen Yang

Keyword(s):

Image Recognition ◽

Depth Image ◽

Shadow Play

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All Three Fear Their Wives (Sanpaqi): A Post-Midnight Shadow Play

Asian Theatre Journal ◽

10.1353/atj.0.0044 ◽

2009 ◽

Vol 26 (2) ◽

pp. 197-214

Author(s):

Fan Pen Chen

Keyword(s):

Shadow Play

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

Computational Intelligence and Neuroscience ◽

10.1155/2021/5584756 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Sahar S. Tabrizi ◽

Saeid Pashazadeh ◽

Vajiheh Javani

Keyword(s):

Evaluation System ◽

Low Cost ◽

Estimation Error ◽

Weighted Average ◽

Principal Component ◽

Training System ◽

Table Tennis ◽

Sports Activity ◽

Nonlinear Regression Models ◽

Shadow Play

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.

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