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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

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/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

13

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

3619396

Kód UT WoS článku

001594893600045

EID výsledku v databázi Scopus

2-s2.0-105018362442