ArticleJournal of healthcare engineering2021
Target Tracking Algorithm for Table Tennis Using Machine Vision.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Table Tennis Track Detection Based on Temporal Feature Multiplexing Network.Sensors (Basel, Switzerland) · 2023Article
- Retracted: Target Tracking Algorithm for Table Tennis Using Machine Vision.Journal of healthcare engineering · 2023Article
- A Web-Based Model to Predict a Neurological Disorder Using ANN.Healthcare (Basel, Switzerland) · 2022Article
- Application of Tactics in Technical and Tactical Analysis of Table Tennis Mixed Doubles Based on Artificial Intelligence Graph Theory Model.Journal of environmental and public health · 2022Article
- Biomechanical Analysis of Different Footwork Foot Movements in Table Tennis.Computational intelligence and neuroscience · 2022Article
- Application of Multiple Regression Analysis Model in Table Tennis Competition.Journal of environmental and public health · 2022Article
Corrections and comments
- Retraction · 2023-05-24Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current table tennis robot system has two common problems. One is the table tennis ball speed, which moves fast, and it is difficult for the robot to react in a short time. The second is that the robot cannot recognize the type of the ball's movement, i.e., rotation, top rotation, no rotation, wait, etc. It is impossible to judge whether the ball is rotating and the direction of rotation, resulting in a single return strategy of the robot with poor adaptability. In this paper, these problems are solved by proposing a target trajectory tracking algorithm for table tennis using machine vision combined with Scaled Conjugate Gradient (SCG). Real human-machine game's data are obtained in the proposed algorithm by extracting ten continuous position information and speed information frames for feature selection. These features are used as input data for the deep neural network and then are normalized to create a deep neural network algorithm model. The model is trained by the position information of the successive 20 frames. During the initial sets of experiments, we found the shortcomings of the original SCG algorithm. By setting the accuracy threshold and offline learning of historical data and saving the hidden layer weight matrix, the SCG algorithm was improved. Finally, experiments verify the improved algorithm's feasibility and applicability and show that the proposed algorithm is more suitable for table tennis robots.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.