Multisensor Fusion for Noninvasive Worker Health Monitoring in Industry 4.0
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Multisensor Fusion for Noninvasive Worker Health Monitoring in Industry 4.0
Original language description
Workers' 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Instrumentation and Measurement
ISSN
0018-9456
e-ISSN
1557-9662
Volume of the periodical
74
Issue of the periodical within the volume
02 June 2025
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
nestránkováno
UT code for WoS article
001518786900024
EID of the result in the Scopus database
2-s2.0-105007289953