ArticlePloS one2023
RETRACTED: A wearable-based sports health monitoring system using CNN and LSTM with self-attentions.
Article in PloS one, 2023. 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 It has been retracted, and should not be counted.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 17 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
17 citing papers in PubMed.
- Deep Learning for Sensor-Based Sport Performance and Health Monitoring: A Review of Wearable, Vision-Based, and Multimodal Sensing Approaches.Sensors (Basel, Switzerland) · 2026Review
- IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis.Scientific reports · 2026Article
- The intention of Chinese college students to use sports smart wearable devices based on the technology acceptance model and technology readiness index.Scientific reports · 2026Article
- Multimodal human action recognition and personalized sports health promotion: a deep learning framework integrating wearable sensor fusion.Frontiers in neurorobotics · 2026Article
- Retraction: A wearable-based sports health monitoring system using CNN and LSTM with self-attentions.PloS one · 2026Article
- Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention.Bioengineering (Basel, Switzerland) · 2025Review
- Physical education teaching design under the STEAM concept using the convolutional neural network.Scientific reports · 2025Article
- Applications of flexible materials in health management assisted by machine learning.RSC advances · 2025Review
- Application of Wearable Insole Sensors in In-Place Running: Estimating Lower Limb Load Using Machine Learning.Biosensors · 2025Article
- Cloud-to-Thing continuum-based sports monitoring system using machine learning and deep learning model.PeerJ. Computer science · 2025Article
- Article
- A deep hybrid learning framework with attention-enhanced feature extraction for BMI prediction based on physical fitness.Frontiers in public health · 2025Article
- Digital health interventions from the humanistic perspective of sports: strategies to promote health for all.Frontiers in public health · 2025Article
- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
- RDA-MTE: an innovative model for emotion recognition in sports behavior decision-making.Frontiers in neuroscience · 2024Article
- From data to decision: Machine learning determination of aerobic and anaerobic thresholds in athletes.PloS one · 2024Article
- Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance.Digital healthArticle
Corrections and comments
- Retraction · 2026-08-10Concerns/Issues about Article · Objections by Author(s) · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/1A20DA31E08BEFB3A1501BF542F86E
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sports performance and health monitoring are essential for athletes to maintain peak performance and avoid potential injuries. In this paper, we propose a sports health monitoring system that utilizes wearable devices, cloud computing, and deep learning to monitor the health status of sports persons. The system consists of a wearable device that collects various physiological parameters and a cloud server that contains a deep learning model to predict the sportsperson's health status. The proposed model combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-attention mechanisms. The model is trained on a large dataset of sports persons' physiological data and achieves an accuracy of 93%, specificity of 94%, precision of 95%, and an F1 score of 92%. The sports person can access the cloud server using their mobile phone to receive a report of their health status, which can be used to monitor their performance and make any necessary adjustments to their training or competition schedule.
Indexed as
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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.