Multisensor Fusion for Noninvasive Worker Health Monitoring in Industry 4.0

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258349" target="_blank" >RIV/61989100:27240/25:10258349 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11021465" target="_blank" >https://ieeexplore.ieee.org/document/11021465</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIM.2025.3576005" target="_blank" >10.1109/TIM.2025.3576005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multisensor Fusion for Noninvasive Worker Health Monitoring in Industry 4.0

Popis výsledku v původním jazyce

Workers&apos; health state monitoring in Industry 4.0 is crucial for the prevention of work-related musculoskeletal disorders (MSDs) caused by repetitive movements. Electromyography (EMG) is commonly used for detecting muscle activity and assessing muscle load. Unfortunately, accurate processing and analysis of EMG remain challenging due to the nonlinear nature of this signal. To enhance the accuracy of muscle activity analysis, this study employs wearable sensors to concurrently monitor electrocardiography (ECG), EMG, accelerometry, gyroscope, and magnetometry using two synchronized Shimmer3 ECG/EMG units. These signals were recorded and analyzed on 13 volunteers during eight different scenarios of repetitive upper limb movements commonly encountered in Industry 4.0 environments. Heart activity was evaluated using ECG, achieving an average accuracy of 88.8% in R-peak detection, with average values of mu and +/- 1.96 sigma being 0.2 and 12.6 bpm, respectively, in heart rate (HR) determination. HR variability (HRV) analysis was conducted, revealing varying degrees of stress or fatigue in most cases. Physical activity detection was performed using two separate approaches: 1) an EMG signal analysis method and 2) a newly introduced accelerometer-gyroscope-magnetometer (AGM) method. The AGM method developed in this study demonstrated superior performance compared to the EMG method, with an average accuracy of 96.4% compared to 45.5% for EMG. Muscle fatigue quantification was effectively performed by analyzing the frequency characteristics of EMG. The results of this study confirmed the potential of wearable sensors for monitoring the health status of workers in Industry 4.0 environments, with a focus on the application of the novel AGM method.

Název v anglickém jazyce

Multisensor Fusion for Noninvasive Worker Health Monitoring in Industry 4.0

Popis výsledku anglicky

Workers&apos; health state monitoring in Industry 4.0 is crucial for the prevention of work-related musculoskeletal disorders (MSDs) caused by repetitive movements. Electromyography (EMG) is commonly used for detecting muscle activity and assessing muscle load. Unfortunately, accurate processing and analysis of EMG remain challenging due to the nonlinear nature of this signal. To enhance the accuracy of muscle activity analysis, this study employs wearable sensors to concurrently monitor electrocardiography (ECG), EMG, accelerometry, gyroscope, and magnetometry using two synchronized Shimmer3 ECG/EMG units. These signals were recorded and analyzed on 13 volunteers during eight different scenarios of repetitive upper limb movements commonly encountered in Industry 4.0 environments. Heart activity was evaluated using ECG, achieving an average accuracy of 88.8% in R-peak detection, with average values of mu and +/- 1.96 sigma being 0.2 and 12.6 bpm, respectively, in heart rate (HR) determination. HR variability (HRV) analysis was conducted, revealing varying degrees of stress or fatigue in most cases. Physical activity detection was performed using two separate approaches: 1) an EMG signal analysis method and 2) a newly introduced accelerometer-gyroscope-magnetometer (AGM) method. The AGM method developed in this study demonstrated superior performance compared to the EMG method, with an average accuracy of 96.4% compared to 45.5% for EMG. Muscle fatigue quantification was effectively performed by analyzing the frequency characteristics of EMG. The results of this study confirmed the potential of wearable sensors for monitoring the health status of workers in Industry 4.0 environments, with a focus on the application of the novel AGM method.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Instrumentation and Measurement

ISSN

0018-9456

e-ISSN

1557-9662

Svazek periodika

74

Číslo periodika v rámci svazku

02 June 2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

nestránkováno

Kód UT WoS článku

001518786900024

EID výsledku v databázi Scopus

2-s2.0-105007289953