Integrated device for the measurement of systemic and local oxygen transport during physical exercise

Luca Pollonini, Rebecca Re, Richard J. Simpson, Clifford Dacso

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Current methods for monitoring exercise exertion rely upon heart rate monitors, which represent a crude and lagging indicator of conditioning. The rationale for the present study is that both systemic and local metabolic mechanisms are responsible for physical performance, and therefore they should be simultaneously quantified to achieve an objective assessment of human conditioning. We propose a compact, wearable near-infrared spectroscopy (NIRS) device integrated with electrocardiography (ECG) and photoplethysmography (PPG) to simultaneously assess the cardiovascular and local response to exercise. The system was tested on subjects performing a graded maximal exercise by comparing our readings with metabolic variables measured with respiratory gas analysis. We found strong correlations between local deoxyhemoglobin concentration [HHb], heart rate and oxygen uptake, as well as between oxyhemoglobin concentration [HbO2] and stroke volume. This study shows that combined NIRS, ECG and PPG measurements yield useful information to understand the interplay between systemic and local muscular responses to exercise.

Original languageEnglish (US)
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages3760-3763
Number of pages4
DOIs
StatePublished - Dec 14 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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