ArticleBiosensors2022
A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.Scientific reports · 2024Article
- Frontiers of Wearable Biosensors for Human Health Monitoring.Biosensors · 2023Article
- Microfluidic Wearable Devices for Sports Applications.Micromachines · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers' physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers' physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers' PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO
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Registered trials
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