ArticleScientific reports2025
IoT-enabled real-time health monitoring system for adolescent physical rehabilitation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
- Revolutionizing spine surgery with emerging AI-FEA integration.Journal of robotic surgery · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to develop an intelligent system leveraging Internet of Thing (IoT) technology to enhance the precision of youth physical training monitoring and improve training outcomes. A wearable device incorporating Micro Electro Mechanical Systems (MEMS) sensors is integrated to collect real-time motion data. Advanced signal processing and filtering techniques are employed to minimize noise interference and improve data accuracy. A particle swarm optimization support vector machine (PSO-SVM) algorithm is utilized to classify motion patterns. To evaluate the system's performance, experiments were conducted to assess motion pattern recognition accuracy, response time, real-time analysis capabilities, and system stability and capacity. The methods we use and the data we collect are from public datasets, do not involve privacy protection for adolescents, and have been approved by the institutional ethics committee. The system demonstrated a motion pattern recognition accuracy exceeding 95% and a response time consistently below 250 ms under various network conditions. Practical applications revealed the system's effectiveness in health monitoring, leading to improved physical fitness and positive rehabilitation outcomes for adolescent patients. This study offers an innovative digital solution for adolescent physical training and health monitoring. The system's strong application potential and valuable insights contribute to the advancement of related research.
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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.