Motion analysis using global navigation satellite system and physiological data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63569198" target="_blank" >RIV/70883521:28140/23:63569198 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/23:00371375 RIV/00216208:11150/23:10473754 RIV/60461373:22340/23:43927699

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10107613" target="_blank" >https://ieeexplore.ieee.org/document/10107613</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3270102" target="_blank" >10.1109/ACCESS.2023.3270102</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Motion analysis using global navigation satellite system and physiological data

Popis výsledku v původním jazyce

Motion analysis using wearable sensors is an essential research topic with broad mathematical foundations and applications in various areas, including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body, along with signals from additional sensors, for monitoring of physical activity and analyzing heart rate dynamics during running on route segments of different slopes and speeds. This method provides an alternative to the heart monitoring on the treadmill ergometer in the cardiology laboratory. The proposed computational methodology involves digital data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The motion patterns detection involves segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This classification method compares neural networks, support vector machine, Bayesian, and k-nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface demonstrate the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems.

Název v anglickém jazyce

Motion analysis using global navigation satellite system and physiological data

Popis výsledku anglicky

Motion analysis using wearable sensors is an essential research topic with broad mathematical foundations and applications in various areas, including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body, along with signals from additional sensors, for monitoring of physical activity and analyzing heart rate dynamics during running on route segments of different slopes and speeds. This method provides an alternative to the heart monitoring on the treadmill ergometer in the cardiology laboratory. The proposed computational methodology involves digital data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The motion patterns detection involves segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This classification method compares neural networks, support vector machine, Bayesian, and k-nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface demonstrate the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

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

11

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

42096-42103

Kód UT WoS článku

000981907000001

EID výsledku v databázi Scopus

2-s2.0-85159687720