Assessment of Mental Workload Level Based on PPG Signal Fusion Continuous Wavelet Transform and Cardiopulmonary Coupling Technology Article Swipe
Han Zhang
,
Ziyi Wang
,
Yan Zhuang
,
Shimin Yin
,
Zhencheng Chen
,
Yongbo Liang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13071238
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13071238
Mental workload is an important predisposing factor for mental illnesses such as depression and is closely related to individual mental health. However, the suboptimal accuracy of utilizing photoplethysmography (PPG) exclusively for mental workload classification has constrained its application within pertinent professional domains. To this end, this paper proposes a signal processing method that combines continuous wavelet transform (CWT) and cardiopulmonary coupling mapping (CPC) to classify mental load via a convolutional neural network (ResAttNet). The method reflects changes in mental workload, as assessed by changes in the association between heart rate variability and respiration. In this paper, the strengths and weaknesses of this method are compared with other traditional psychological workload monitoring methods, such as heart rate variability (HRV), and its validation is performed on the publicly available dataset MAUS. The experiments show that the method is significantly better than previous machine learning methods based on heart rate variability correlation. Meanwhile, the accuracy of the method proposed in this paper reaches 80.5%, which is 6.2% higher than in previous studies. It is comparable to the result of 82.4% for the ECG-based mental workload monitoring system. Therefore, the method of combining CWT and CPC has considerable potential and provides new ideas for mental workload classification.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13071238
- https://www.mdpi.com/2079-9292/13/7/1238/pdf?version=1711537229
- OA Status
- gold
- Cited By
- 5
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393235325
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- OpenAlex ID
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https://openalex.org/W4393235325Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics13071238Digital Object Identifier
- Title
-
Assessment of Mental Workload Level Based on PPG Signal Fusion Continuous Wavelet Transform and Cardiopulmonary Coupling TechnologyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-27Full publication date if available
- Authors
-
Han Zhang, Ziyi Wang, Yan Zhuang, Shimin Yin, Zhencheng Chen, Yongbo LiangList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics13071238Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/13/7/1238/pdf?version=1711537229Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2079-9292/13/7/1238/pdf?version=1711537229Direct OA link when available
- Concepts
-
Workload, Heart rate variability, Computer science, Wavelet transform, Artificial intelligence, Convolutional neural network, Wavelet, Photoplethysmogram, Pattern recognition (psychology), Data mining, Heart rate, Computer vision, Medicine, Filter (signal processing), Blood pressure, Radiology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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