Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
With the progress of society and the improvement of living standards, sports training has gradually become an area of increasing concern for society and individuals. To more comprehensively grasp the physical function, body shape, and physical fitness of athletes, many researchers have conducted extensive research on the real-time detection of human body nutrition. This study is mainly supported by cloud computing and somatosensory network technology, and the real-time detection of human body composition in sports training is the main research object. In the experiment, two methods of human body composition detection were tested: the BIA method and the body composition analysis method based on the electrochemical sensor of body sweat. It designed a human nutrient composition detection system based on the BIA method. The error rate of the system is relatively small, which is basically maintained at about 2%. It uses a body surface sweat electrochemical sensor to detect changes in glucose concentration during human exercise. After exercising for a period of time, the test subject's sweat glucose concentration remained around 0.5 mM.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033316 | PMC |
| http://dx.doi.org/10.1155/2022/9911905 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deep Learning-Assisted Organogel Pressure Sensor for Alphabet Recognition and Bio-Mechanical Motion Monitoring.
Nanomicro Lett
September 2025
Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, Republic of Korea.
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti-freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol-gelatin (PVA/GLE) matrix.
View Article and Find Full Text PDFLabel-Free Dynamic Single-Molecule Sensing of Alpha-2-Macroglobulin in Complex Matrices.
ACS Sens
September 2025
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Alpha-2-macroglobulin (A2M) is a critical biomarker implicated in inflammation, immune regulation, coagulation, and various pathological conditions such as liver fibrosis, neurodegenerative diseases, and cancers. However, its precise quantification remains challenging due to complex conformational dynamics, subtle abundance fluctuations, and interference from plasma proteins. Here, we present a label-free dynamic single-molecule sensing (LFDSMS) strategy for the sensitive and specific detection of A2M.
View Article and Find Full Text PDFBackground: To improve the molecular diagnostic yield for Aspergillus spp. from respiratory samples, we developed and evaluated a new DNA extraction method directly from respiratory samples combined with in-house Aspergillus real-time PCR.
Methods: We developed a method using beads and resin, where a sample is centrifuged to separate the supernatant and pellet.
Cherry-Net: real-time segmentation algorithm of cherry maturity based on improved PIDNet.
Front Plant Sci
September 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Accurate identification of cherry maturity and precise detection of harvestable cherry contours are essential for the development of cherry-picking robots. However, occlusion, lighting variation, and blurriness in natural orchard environments present significant challenges for real-time semantic segmentation.
Methods: To address these issues, we propose a machine vision approach based on the PIDNet real-time semantic segmentation framework.
Analyzing Depression in College Students Using NLP and Transformer Models: Implications for Career and Educational Counseling.
Brain Behav
September 2025
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
Purpose: Depression among college students is a growing concern that negatively affects academic performance, emotional well-being, and career planning. Existing diagnostic methods are often slow, subjective, and inaccessible, underscoring the need for automated systems that can detect depressive symptoms through digital behavior, particularly on social media platforms.
Method: This study proposes a novel natural language processing (NLP) framework that combines a RoBERTa-based Transformer with gated recurrent unit (GRU) layers and multimodal embeddings.