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使用心率监测的个体化和时变减压模型。

Use of heart rate monitoring for an individualized and time-variant decompression model.

机构信息

Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.

出版信息

Eur J Appl Physiol. 2010 Nov;110(5):885-92. doi: 10.1007/s00421-010-1542-3. Epub 2010 Jun 25.

Abstract

Individual differences, physiological pre-conditions and in-dive conditions like workload and body temperature have been known to influence bubble formation and risk of decompression sickness in diving. Despite this fact, such effects are currently omitted from the decompression algorithms and tables that are aiding the divers. There is an apparent need to expand the modeling beyond depth and time to increase safety and efficiency of diving. The present paper outlines a mathematical model for how heart rate monitoring in combination with individual parameters can be used to obtain a customized and time-variant decompression model. We suggest that this can cover some of the individual differences and dive conditions that are affecting bubble formation. The model is demonstrated in combination with the previously published Copernicus decompression model, and is suitable for implementation in dive computers and post dive simulation software for more accurate risk analysis.

摘要

个体差异、生理前提条件和潜水条件,如工作负荷和体温,已被证明会影响潜水时的气泡形成和减压病风险。尽管如此,这些影响目前仍被排除在减压算法和表格之外,这些算法和表格是在帮助潜水员。显然需要将模型扩展到深度和时间之外,以提高潜水的安全性和效率。本文概述了一种数学模型,用于结合个体参数如何使用心率监测来获得定制的、随时间变化的减压模型。我们认为,这可以涵盖一些影响气泡形成的个体差异和潜水条件。该模型与之前发布的哥白尼减压模型结合使用,适合在潜水电脑和潜水后模拟软件中实现,以进行更准确的风险分析。

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