ArticleComputational intelligence and neuroscience2021
A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review.Sports medicine (Auckland, N.Z.) · 2023Pooled it
- A multimodal deep learning-based model for posture asymmetry recognition and sports injury risk prediction in adolescent table tennis athletes.Frontiers in physiology · 2026Article
- Wearable sensing for badminton stroke recognition with one-dimensional convolutional neural network.Scientific reports · 2025Article
- Assessing and Enhancing Movement Quality Using Wearables and Consumer Technologies: Thematic Analysis of Expert Perspectives.JMIR formative research · 2024Article
- Machine learning approach for the detection of vitamin D level: a comparative study.BMC medical informatics and decision making · 2023Article
- Artificial Intelligence Technologies and Their Application for Reform and Development of Table Tennis Training in Complex Environments.Computational intelligence and neuroscience · 2022Article
- Biomechanical Analysis of Different Footwork Foot Movements in Table Tennis.Computational intelligence and neuroscience · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.