Integrated Biomechanical Motion Analysis in a Virtual Cycling Environment Using Wearable Sensors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064203%3A_____%2F25%3A10504272" target="_blank" >RIV/00064203:_____/25:10504272 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/25:00385701 RIV/70883521:28140/25:63597920 RIV/60461373:22340/25:43932962

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=hIP80k812r" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=hIP80k812r</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3619396" target="_blank" >10.1109/ACCESS.2025.3619396</a>

Result language

angličtina

Original language name

Integrated Biomechanical Motion Analysis in a Virtual Cycling Environment Using Wearable Sensors

Original language description

Biomechanical motion analysis in a virtual cycling environment through the inertial measurement units (IMU) forms a specific approach to movement assessment integrating accelerometric and gyrometric sensors. The paper provides comprehensive data for evaluating physical activity, monitoring rehabilitation exercises, assessing neurological conditions, and detecting cardiological abnormalities. The dataset comprises 50 experiments and recordings from five distinct virtual cycling tours with varying altitude profiles, collectively spanning over 1,100 kilometers. The proposed methodology includes automated segmentation of cycling routes based on slope variation, extraction of statistical and frequency features from physiological, accelerometric, and gyrometric signals, and their subsequent classification using signal processing and computational intelligence techniques. Analysis of 3,526 segmented intervals revealed significant correlations between heart rate variations and slope gradients, as well as estimations of motion symmetry coefficients relevant to biomechanical assessment. The classification accuracy reached 95.5% for motion and physiological features, and 85.6% for gyrometric data using the two-layer neural network model across different slope conditions. The findings demonstrate the potential of hybrid systems combining wearable sensors and virtual environments for advanced motion analysis. This work underscores the applicability of general-purpose digital signal processing methods and machine learning algorithms in the multichannel analysis of physiological data, with applications in neurology, rehabilitation, and telemedicine.

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/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

13

Issue of the periodical within the volume

October

Country of publishing house

US - UNITED STATES

Number of pages

9

Pages from-to

3619396

UT code for WoS article

001594893600045

EID of the result in the Scopus database

2-s2.0-105018362442