Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30502 - Other medical science

Project

<a href="/en/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Center for the Development of Personalized Medicine in Age-Related Diseases</a><br>

Continuities

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

Publication year

2020

Confidentiality

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

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

—

Volume of the periodical

20

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

1523

UT code for WoS article

000525271500285

EID of the result in the Scopus database

2-s2.0-85081627422