ArticleApplied bionics and biomechanics2022
Information Collection, Analysis, and Monitoring System of Children's Physical Training Based on Multisensor.
Article in Applied bionics and biomechanics, 2022. 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 It has been retracted, and should not be counted.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 1 paper.
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
1 citing paper in PubMed.
- Retracted: Information Collection, Analysis, and Monitoring System of Children's Physical Training Based on Multisensor.Applied bionics and biomechanics · 2023Article
Corrections and comments
- Retraction · 2023-08-16Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to obtain more children's physical training information and improve the accuracy of children's physical training monitoring, a multisensor-based children's physical training information collection, analysis, and monitoring system is proposed. In the process of physical training and sports training, people's physical training information collection is directly related to the level and effectiveness of physical training. With the combination of multisensor concept and sports training information collection, it can collect the key index data of sports mobilization in real time with the help of multiple sensors and information technology. Taking children's physical training as the object, this paper designs a multisensor physical training data information acquisition terminal, collects different training characteristic data with the help of multisensor equipment, and then comprehensively analyzes and monitors the physical information with the help of certain fusion technology, so as to construct a human posture recognition algorithm based on children's physical training information acquisition. Support vector machine and decision tree are used to classify children's different physical exercise states, and a relatively perfect algorithm architecture of human posture recognition is constructed. The results show that for two decision trees, each decision tree is trained with a total of 675 groups of data, and a total of 342 groups of data are verified and pruned. The two decision trees take 7.17 s and 7.32 s to complete the training process, respectively. It can be seen that when the number of training groups is equal, the training time of the two placement methods is close, so it can be considered that the two placement methods have little effect on the training speed of decision tree. The experimental data show that the design of children's physical training monitoring system in this paper has a certain market value.
Identifiers
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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.