Chrome Extension
WeChat Mini Program
Use on ChatGLM
VIPCreated with Sketch.

    • Methodology and Sensor Technology for Hydration Monitoring

    Neruna Yugarajah,Rainer Brueck,Alexander Keil

    ADVANCES IN DIGITAL HEALTH AND MEDICAL BIOENGINEERING, VOL 1, EHB-2023（2024）

    Univ Siegen

    Cited 0|Views1
    Abstract
    This paper explores methods for monitoring hydration, including biochemical, physical, and multisensory approaches, emphasizing their strengths and limitations. It raises the question of continuous hydration monitoring feasibility and discusses promising technological advances. The need for nuanced, context-specific choices in hydration monitoring methods is highlighted. Future research should focus on wearable technology integration to enhance accuracy and practicality. Hydration monitoring significantly impacts human well-being and performance.
    More
    Translated text
    Key words
    Hydration monitoring,Biochemical methods,Physical methods,Multisensor technology,Continuous monitoring
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    GPU is busy, summary generation fails
    Rerequest