Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living

被引:0
|
作者
T. Beltrame
R. Amelard
A. Wong
R. L. Hughson
机构
[1] Faculty of Applied Health Sciences,Departamento de Fisioterapia
[2] University of Waterloo,Department of Systems Design Engineering
[3] Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq),undefined
[4] Universidade Ibirapuera,undefined
[5] São Paulo,undefined
[6] Brazil,undefined
[7] University of Waterloo,undefined
[8] Schlegel-University of Waterloo Research Institute for Aging,undefined
来源
Scientific Reports | / 7卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Currently, oxygen uptake ([inline-graphic not available: see fulltext]) is the most precise means of investigating aerobic fitness and level of physical activity; however, [inline-graphic not available: see fulltext] can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict [inline-graphic not available: see fulltext] during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the [inline-graphic not available: see fulltext] dynamics during ADL. The variables derived from the wearable sensors were used to create a [inline-graphic not available: see fulltext] predictor based on a random forest method. The [inline-graphic not available: see fulltext] temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted [inline-graphic not available: see fulltext] during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured [inline-graphic not available: see fulltext] and the prediction bias was 0.2 ml·min−1·kg−1. The MNG calculated based on predicted [inline-graphic not available: see fulltext] was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured [inline-graphic not available: see fulltext] data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.
引用
收藏
相关论文
共 50 条
  • [1] Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living
    Beltrame, T.
    Amelard, R.
    Wong, A.
    Hughson, R. L.
    SCIENTIFIC REPORTS, 2017, 7
  • [2] Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk
    Kantoch, Eliasz
    SENSORS, 2018, 18 (10)
  • [3] Prediction of energy expenditure during activities of daily living by a wearable set of inertial sensors
    Hedegaard, Mathias
    Anvari-Moghaddam, Amjad
    Jensen, Bjorn K.
    Jensen, Cecilie B.
    Pedersen, Mads K.
    Samani, Afshin
    MEDICAL ENGINEERING & PHYSICS, 2020, 75 : 13 - 22
  • [4] Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models
    Beltrame, Thomas
    Amelard, Robert
    Wong, Alexander
    Hughson, Richard L.
    JOURNAL OF APPLIED PHYSIOLOGY, 2018, 124 (02) : 473 - 481
  • [5] Investigating the Performance of Wearable Motion Sensors on recognizing falls and daily activities via machine learning
    Kavuncuoglu, Erhan
    Uzunhisarcikli, Esma
    Barshan, Billur
    Ozdemir, Ahmet Turan
    DIGITAL SIGNAL PROCESSING, 2022, 126
  • [6] Testing and Analysis of Activities of Daily Living Data with Machine Learning Algorithms
    Cufoglu, Ayse
    Coskun, Adem
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2016, 7 (03) : 436 - 441
  • [7] A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors
    Nisar, Muhammad Adeel
    Shirahama, Kimiaki
    Irshad, Muhammad Tausif
    Huang, Xinyu
    Grzegorzek, Marcin
    SENSORS, 2023, 23 (19)
  • [8] Estimating Oxygen Uptake During Nonsteady-State Activities and Transitions Using Wearable Sensors
    Altini, Marco
    Penders, Julien
    Amft, Oliver
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2016, 20 (02) : 469 - 475
  • [9] Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
    Murtadha D. Hssayeni
    Joohi Jimenez-Shahed
    Michelle A. Burack
    Behnaz Ghoraani
    Scientific Reports, 11
  • [10] Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
    Hssayeni, Murtadha D.
    Jimenez-Shahed, Joohi
    Burack, Michelle A.
    Ghoraani, Behnaz
    SCIENTIFIC REPORTS, 2021, 11 (01)