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LUP Student Papers

LUND UNIVERSITY LIBRARIES

Real time non-invasive estimation of oxygen uptake using smartphones

Lund, Arvid LU (2023) MAMM10 20231
Ergonomics and Aerosol Technology
Abstract
Oxygen uptake is a great indicator of cardiopulmonary health but requires spe-
cialized equipment and is time consuming to measure. There are ways to estimate
oxygen uptake based on other factors such as heart rate and acceleration. The
aim of this master thesis was to investigate whether models estimating oxygen
uptake based on acceleration data collected from the sensors in smartphones,
and steps and heart rate data from wearables could be created.
To do this ten healthy volunteers performed a cardiopulmonary exercise test
(CPET) where their oxygen uptake was measured using a portable system in ad-
dition to this acceleration was measured using both an MSR 165 accelerometer
and a smartphone, and their heart rate was measured... (More)
Oxygen uptake is a great indicator of cardiopulmonary health but requires spe-
cialized equipment and is time consuming to measure. There are ways to estimate
oxygen uptake based on other factors such as heart rate and acceleration. The
aim of this master thesis was to investigate whether models estimating oxygen
uptake based on acceleration data collected from the sensors in smartphones,
and steps and heart rate data from wearables could be created.
To do this ten healthy volunteers performed a cardiopulmonary exercise test
(CPET) where their oxygen uptake was measured using a portable system in ad-
dition to this acceleration was measured using both an MSR 165 accelerometer
and a smartphone, and their heart rate was measured using a Fitbit Edge 4. Dif-
ferent ways to process the raw acceleration data were used to create models link-
ing acceleration to oxygen uptake. The models were compared to each other to
investigate which model best estimated oxygen uptake.
The results show that models using the L2 norm of the acceleration, with
the effect of gravity removed, performed better than models using the integral
of absolute acceleration. Additionally, the models based on data collected using
the smartphone outperformed the models based on data from a purpose made
accelerometer. A model based on 6-axis motion data collected using the smart-
phone performed the best with an R^2 value of 0.98 showing great potential for
estimating oxygen uptake using only a smartphone. (Less)
Abstract (Swedish)
Syreupptagningsförmåga är en bra indikator av kardiopulmonär hälsa, men be-
höver speciell utrustning och är tidskrävande att mäta. Det finns sätt att upp-
skatta syreupptagningsförmåga baserat på andra faktorer så som puls och accele-
ration. Målet med denna masteruppsats var att undersöka om modeller som upp-
skattar syreupptag baserat på accelerationsdata insamlad från sensorer i smartp-
hones, samt steg- och hjärtfrekvensdata från en bärbar enhet, kunde skapas.
För att göra detta utförde tio friska försökspersoner ett kardiopulmonärt
ansträngningstest (CPET) där deras syreupptag mättes med hjälp av ett portabelt
system. Dessutom mättes acceleration med både en MSR 165 accelerometer och
en smartphone, och deras hjärtfrekvens... (More)
Syreupptagningsförmåga är en bra indikator av kardiopulmonär hälsa, men be-
höver speciell utrustning och är tidskrävande att mäta. Det finns sätt att upp-
skatta syreupptagningsförmåga baserat på andra faktorer så som puls och accele-
ration. Målet med denna masteruppsats var att undersöka om modeller som upp-
skattar syreupptag baserat på accelerationsdata insamlad från sensorer i smartp-
hones, samt steg- och hjärtfrekvensdata från en bärbar enhet, kunde skapas.
För att göra detta utförde tio friska försökspersoner ett kardiopulmonärt
ansträngningstest (CPET) där deras syreupptag mättes med hjälp av ett portabelt
system. Dessutom mättes acceleration med både en MSR 165 accelerometer och
en smartphone, och deras hjärtfrekvens mättes med hjälp av en Fitbit Edge 4.
Olika sätt att behandla rå accelerationsdata användes för att skapa modeller som
kopplade acceleration till syreupptag. Dessa modeller jämfördes med varandra
och med modeller skapade med hjärtfrekvens- och stegdata från en Fitbit.
Resultaten visar att modeller som använder L2-normen för acceleration, med
gravitation borttagen, presterade bättre än modeller som använde integralen av
absolut acceleration. Dessutom överträffade modellerna baserade på data insam-
lad med smartphones de modeller som baserades på data från MSR 165 accelero-
metern. En modell baserad på 6-axlig rörelsedata, insamlad med smartphones
presterade bäst med en R^2-värde på 0,98 och visade stor potential för att upp-
skatta syreupptag med endast en smartphone. (Less)
Popular Abstract (Swedish)
Sensorer i mobiltelefoner visar lovande resultat som ett nytt sätt att i realtid uppskatta syreupptagning.
Please use this url to cite or link to this publication:
author
Lund, Arvid LU
supervisor
organization
alternative title
Icke-invasiv realtidsuppskattning av syreupptag med hjälp av mobiltelefoner
course
MAMM10 20231
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Oxygen uptake, Acceleration, Smartphone, Estimation, Health
language
English
id
9137074
date added to LUP
2023-09-21 09:30:44
date last changed
2023-09-21 09:30:44
@misc{9137074,
  abstract     = {{Oxygen uptake is a great indicator of cardiopulmonary health but requires spe-
cialized equipment and is time consuming to measure. There are ways to estimate
oxygen uptake based on other factors such as heart rate and acceleration. The
aim of this master thesis was to investigate whether models estimating oxygen
uptake based on acceleration data collected from the sensors in smartphones,
and steps and heart rate data from wearables could be created.
To do this ten healthy volunteers performed a cardiopulmonary exercise test
(CPET) where their oxygen uptake was measured using a portable system in ad-
dition to this acceleration was measured using both an MSR 165 accelerometer
and a smartphone, and their heart rate was measured using a Fitbit Edge 4. Dif-
ferent ways to process the raw acceleration data were used to create models link-
ing acceleration to oxygen uptake. The models were compared to each other to
investigate which model best estimated oxygen uptake.
The results show that models using the L2 norm of the acceleration, with
the effect of gravity removed, performed better than models using the integral
of absolute acceleration. Additionally, the models based on data collected using
the smartphone outperformed the models based on data from a purpose made
accelerometer. A model based on 6-axis motion data collected using the smart-
phone performed the best with an R^2 value of 0.98 showing great potential for
estimating oxygen uptake using only a smartphone.}},
  author       = {{Lund, Arvid}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Real time non-invasive estimation of oxygen uptake using smartphones}},
  year         = {{2023}},
}