Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F18%3A63519836" target="_blank" >RIV/70883521:28140/18:63519836 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/18:00328956 RIV/00216208:11150/18:10381466 RIV/60461373:22340/18:43915773

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8352748/authors#authors" target="_blank" >https://ieeexplore.ieee.org/document/8352748/authors#authors</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TNSRE.2018.2831444" target="_blank" >10.1109/TNSRE.2018.2831444</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing

Popis výsledku v původním jazyce

Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s).

Název v anglickém jazyce

Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing

Popis výsledku anglicky

Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

ISSN

1534-4320

e-ISSN

—

Svazek periodika

26

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

1209-1214

Kód UT WoS článku

000438078700011

EID výsledku v databázi Scopus

2-s2.0-85046376219