Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F20%3A10418400" target="_blank" >RIV/00216208:11150/20:10418400 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/20:00347304 RIV/00179906:_____/20:10418400 RIV/70883521:28140/20:63525253 RIV/60461373:22340/20:43920951

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=_rb5roMQuO" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=_rb5roMQuO</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s20051523" target="_blank" >10.3390/s20051523</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

Popis výsledku v původním jazyce

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands &lt; 3,8 &gt; and &lt; 8,15 &gt; Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

Název v anglickém jazyce

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

Popis výsledku anglicky

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands &lt; 3,8 &gt; and &lt; 8,15 &gt; Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30502 - Other medical science

Projekt

<a href="/cs/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Centrum rozvoje personalizované medicíny u věkem podmíněných onemocnění</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sensors

ISSN

1424-8220

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

1523

Kód UT WoS článku

000525271500285

EID výsledku v databázi Scopus

2-s2.0-85081627422