• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

耗氧量的动态特征。

Dynamic characteristics of oxygen consumption.

机构信息

School of Biomedical Engineering, University of Technology Sydney, 15 Broadway, Sydney, Australia.

School of Electrical Engineering, University of New South Wales, Sydney, Australia.

出版信息

Biomed Eng Online. 2018 Apr 23;17(1):44. doi: 10.1186/s12938-018-0476-6.

DOI:10.1186/s12938-018-0476-6
PMID:29685173
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5914074/
Abstract

BACKGROUND

Previous studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation.

METHODS

Twenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods.

RESULTS

Several kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]).

CONCLUSIONS

The proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO-Speed system. Furthermore, the identified average nonparametric model method can dynamically predict [Formula: see text] response with acceptable accuracy during treadmill exercise.

摘要

背景

先前的研究表明，摄氧量（[Formula: see text]）是评估运动心肺反应最准确的指标之一。在大多数现有研究中，由于刺激不足和信噪比低，[Formula: see text]的反应通常被粗略地建模为一阶系统。为了克服这一困难，本文提出了一种新的基于核的非参数方法，用于动态建模[Formula: see text]对提供更稳健的估计。

方法

20 名健康的非运动员参与者进行了单调刺激（例如，单步功能作为输入）的跑步机运动。在运动过程中，通过流行的便携式气体分析仪（[Formula: see text]，COSMED）测量和记录[Formula: see text]。基于记录的数据，提出了一种基于核的估计方法来对[Formula: see text]进行非参数建模。对于所提出的方法，适当选择的核可以表示先验建模信息，以减少对全面刺激的依赖。此外，由于[Formula: see text]范数和核化[Formula: see text]范数形成的特殊弹性网，估计值是平滑和简洁的。此外，与经典方法相比，所提出的方法基于有限脉冲响应的非参数模型可以最优地选择阶数并更好地拟合。

结果

引入了几种核用于基于核的[Formula: see text]建模方法。结果清楚地表明，稳定样条（SS）核对于[Formula: see text]建模具有最佳性能。特别是，基于 20 名参与者的实验数据，与基准方法（即预测误差法（PEM））相比，所提出的方法具有 SS 核的估计响应明显更好[Formula: see text]（[Formula: see text]与[Formula: see text]）。

结论

所提出的非参数建模方法是估计 VO-Speed 系统脉冲响应的有效方法。此外，所识别的平均非参数模型方法可以在跑步机运动期间以可接受的精度动态预测[Formula: see text]响应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/ac5b70ef84ef/12938_2018_476_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/0695efa9bb27/12938_2018_476_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/7742b2f696e8/12938_2018_476_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/1a61036d8a96/12938_2018_476_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/c209edcad181/12938_2018_476_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/c3e827ed5c53/12938_2018_476_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/e58a78e5562b/12938_2018_476_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/e400f99de28a/12938_2018_476_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/53f9a07a010b/12938_2018_476_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/3d79cc80bf6b/12938_2018_476_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/1c9f10bbf9f2/12938_2018_476_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/6891cd6d2e88/12938_2018_476_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/5274e48041d0/12938_2018_476_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/724d3ffd7beb/12938_2018_476_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/ac5b70ef84ef/12938_2018_476_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/0695efa9bb27/12938_2018_476_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/7742b2f696e8/12938_2018_476_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/1a61036d8a96/12938_2018_476_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/c209edcad181/12938_2018_476_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/c3e827ed5c53/12938_2018_476_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/e58a78e5562b/12938_2018_476_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/e400f99de28a/12938_2018_476_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/53f9a07a010b/12938_2018_476_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/3d79cc80bf6b/12938_2018_476_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/1c9f10bbf9f2/12938_2018_476_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/6891cd6d2e88/12938_2018_476_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/5274e48041d0/12938_2018_476_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/724d3ffd7beb/12938_2018_476_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a902/5914074/ac5b70ef84ef/12938_2018_476_Fig14_HTML.jpg

相似文献

1
Dynamic characteristics of oxygen consumption.耗氧量的动态特征。
Biomed Eng Online. 2018 Apr 23;17(1):44. doi: 10.1186/s12938-018-0476-6.
2
Nonparametric modelling of VO response to exercise.运动时VO反应的非参数建模。
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1525-1528. doi: 10.1109/EMBC.2017.8037126.
3
Nonparametric dynamical model of cardiorespiratory responses at the onset and offset of treadmill exercises.在跑步机运动开始和结束时的心肺反应的非参数动力学模型。
Med Biol Eng Comput. 2018 Dec;56(12):2337-2351. doi: 10.1007/s11517-018-1860-x. Epub 2018 Jun 28.
4
Prediction of peak oxygen uptake in children using submaximal ratings of perceived exertion during treadmill exercise.使用跑步机运动期间次最大自感用力度评分预测儿童的最大摄氧量。
Eur J Appl Physiol. 2016 Jun;116(6):1189-95. doi: 10.1007/s00421-016-3377-z. Epub 2016 Apr 22.
5
Oxygen Uptake and Muscle Deoxygenation Kinetics During Skating: Comparison Between Slide-Board and Treadmill Skating.滑行时的摄氧量和肌肉去氧动力学：滑垫和跑步机滑行之间的比较。
Int J Sports Physiol Perform. 2018 Jul 1;13(6):783-788. doi: 10.1123/ijspp.2017-0440. Epub 2018 Jul 4.
6
Non-invasive estimation of muscle oxygen uptake kinetics with pseudorandom binary sequence and step exercise responses.应用伪随机二进制序列和阶跃运动反应无创估计肌肉氧摄取动力学。
Eur J Appl Physiol. 2018 Feb;118(2):429-438. doi: 10.1007/s00421-017-3785-8. Epub 2017 Dec 18.
7
Estimation methods to detect changes in cardiorespiratory fitness due to exercise training and subsequent detraining.检测因运动训练及随后的停训导致的心肺适能变化的评估方法。
Eur J Appl Physiol. 2023 Apr;123(4):877-889. doi: 10.1007/s00421-022-05113-z. Epub 2022 Dec 23.
8
Oxygen uptake and heart rate kinetics during dynamic upper and lower body exercise: an investigation by time-series analysis.动态上下肢运动期间的摄氧量和心率动力学：一项时间序列分析研究。
Eur J Appl Physiol. 2015 Aug;115(8):1665-72. doi: 10.1007/s00421-015-3146-4. Epub 2015 Mar 13.
9
A specific prediction equation is necessary to estimate peak oxygen uptake in obese patients with metabolic syndrome.需要一个特定的预测方程来估计患有代谢综合征的肥胖患者的最大摄氧量。
J Endocrinol Invest. 2016 Jun;39(6):635-42. doi: 10.1007/s40618-015-0411-7. Epub 2015 Dec 22.
10
Multilevel allometric modelling of maximum cardiac output, maximum arteriovenous oxygen difference, and peak oxygen uptake in 11-13-year-olds.11-13 岁儿童最大心输出量、最大动静脉血氧差和峰值摄氧量的多层次比例模型。
Eur J Appl Physiol. 2020 Feb;120(2):527-537. doi: 10.1007/s00421-020-04300-0. Epub 2020 Jan 10.

引用本文的文献

1
Dose variability of supplemental oxygen therapy with open patient interfaces based on in vitro measurements using a physiologically realistic upper airway model.基于使用生理上逼真的上呼吸道模型的体外测量，开放患者接口的补充氧气治疗的剂量变异性。
Respir Res. 2019 Jul 12;20(1):149. doi: 10.1186/s12931-019-1104-0.

本文引用的文献

1
An equivalent circuit model for onset and offset exercise response.运动起始和结束反应的等效电路模型。
Biomed Eng Online. 2014 Oct 18;13:145. doi: 10.1186/1475-925X-13-145.
2
Mechanisms responsible for the acceleration of pulmonary V̇O2 on-kinetics in humans after prolonged endurance training.长期耐力训练后人类肺 V̇O2 动力学加速的机制。
Am J Physiol Regul Integr Comp Physiol. 2014 Nov 1;307(9):R1101-14. doi: 10.1152/ajpregu.00046.2014. Epub 2014 Aug 27.
3
Modelling and regulating of cardio-respiratory response for the enhancement of interval training.
用于增强间歇训练的心肺反应建模与调节
Biomed Eng Online. 2014 Feb 6;13:9. doi: 10.1186/1475-925X-13-9.
4
Oxygen uptake kinetics.摄氧量动力学。
Compr Physiol. 2012 Apr;2(2):933-96. doi: 10.1002/cphy.c100072.
5
Exercise: Kinetic considerations for gas exchange.运动：气体交换的动力学考虑。
Compr Physiol. 2011 Jan;1(1):203-44. doi: 10.1002/cphy.c090010.
6
Oxygen uptake estimation in humans during exercise using a Hammerstein model.使用哈默斯坦模型估算人类运动期间的摄氧量。
Ann Biomed Eng. 2007 Nov;35(11):1898-906. doi: 10.1007/s10439-007-9362-2. Epub 2007 Aug 9.
7
Identification and control for heart rate regulation during treadmill exercise.跑步机运动期间心率调节的识别与控制。
IEEE Trans Biomed Eng. 2007 Jul;54(7):1238-46. doi: 10.1109/TBME.2007.890738.
8
Effects of load carriage, load position, and walking speed on energy cost of walking.负荷携带、负荷位置和步行速度对步行能量消耗的影响。
Appl Ergon. 2004 Jul;35(4):329-35. doi: 10.1016/j.apergo.2004.03.008.
9
Faster adjustment to and recovery from submaximal exercise in the trained state.在训练状态下对亚最大运动的更快适应和恢复。
J Appl Physiol Respir Environ Exerc Physiol. 1980 Feb;48(2):218-24. doi: 10.1152/jappl.1980.48.2.218.
10
Parameters of ventilatory and gas exchange dynamics during exercise.运动期间通气和气体交换动力学参数。
J Appl Physiol Respir Environ Exerc Physiol. 1982 Jun;52(6):1506-13. doi: 10.1152/jappl.1982.52.6.1506.