Abstract
Highly complex cognitive works require more brain power. The productivity of a person suffers due to this strain, which is sometimes referred to as a mental burden or psychological load. A person’s mental health and safety in high-stress working conditions can be improved with the help of mental workload assessment. A photoplethysmogram (PPG) signal is a non-invasive and easily acquired physiological signal that contains information related to blood volume changes in the micro-vascular bed of tissues and can indicate psychologically relevant information to assess a person’s mental workload (MW). An individual under a high MW possesses an increase in sympathetic nervous system activity, which results in morphological changes in the PPG waveform. In this work, a time-frequency analysis framework is developed to capture these distinguishing PPG features for the automatic assessment of MW. In particular, a cross-wavelet coherence (WTC) approach is proposed to extract simultaneous time-frequency information of the PPG during MW relative to the resting PPG. The suggested technique is validated on a publicly available data set of 22 healthy individuals who took part in an N-back task with PPG recording. Under three different fixed window lengths, images are obtained using WTC between PPG records during N-back task activity and rest. The images are used further to obtain PPG classification in two broad classes of low and high MW using a customized pre-trained Inception-V3 model. The best validation and test accuracy of 93.86% and 93.07%, respectively obtained in the window setting of 1200 samples used for WTC image creation.
Similar content being viewed by others
References
Gupta S, Singh A, Sharma A, Tripathy RK. Higher order derivative-based integrated model for cuff-less blood pressure estimation and stratification using PPG signals. IEEE Sens J. 2022;22(22):22030–9.
Gupta S, Singh A, Sharma A. Exploiting moving slope features of PPG derivatives for estimation of mean arterial pressure. Biomed Eng Lett. 2022;13:1–9.
Chang H-H, et al. A method for respiration rate detection in wrist PPG signal using Holo-Hilbert spectrum. IEEE Sens J. 2018;18(18):7560–9. https://doi.org/10.1109/JSEN.2018.2855974.
Gupta S, Singh A, Sharma A, Tripathy RK. DSVRI: a PPG-based novel feature for early diagnosis of type-II diabetes mellitus. IEEE Sens Lett. 2022;6(9):1–4.
Shresth G, Anurag S, Abhishek S. CIsense: an automated framework for early screening of cerebral infarction using PPG sensor data. Biomed Eng Lett. 2023;14:199–207.
Schaule F, Johanssen JO, Bruegge B, Loftness V. Employing consumer wearables to detect office workers’ cognitive load for interruption management. Proc ACM Interact Mobile Wearable Ubiquitous Technol. 2018;2(1):1–20.
Jaiswal D, Chowdhury A, Chatterjee D, Gavas R. Unobtrusive smart-watch based approach for assessing mental workload. In IEEE Region 10 Symposium (TENSYMP). IEEE. 2019;2019:304–9.
Beh W-K, Wu Y-H, Andy A-YW. Robust PPG-based mental workload assessment system using wearable devices. IEEE J Biomed Health Inform. 2021;27:2323–33.
Aydemir T, Şahin M, Aydemir O. Sequential forward mother wavelet selection method for mental workload assessment on N-back task using photoplethysmography signals. Infrared Phys Technol. 2021;119: 103966.
Kakkos I, et al. Mental workload drives different reorganizations of functional cortical connectivity between 2D and 3D simulated flight experiments. IEEE Trans Neural Syst Rehabil Eng. 2019;27(9):1704–13. https://doi.org/10.1109/TNSRE.2019.2930082.
Rubio S, Díaz E, Martín J, Puente JM. Evaluation of subjective mental workload: a comparison of SWAT, NASA-TLX, and workload profile methods. Appl Psychol. 2004;53(1):61–86.
Pergher V, Wittevrongel B, Tournoy J, Schoenmakers B, Hulle MMV. Mental workload of young and older adults gauged with ERPs and spectral power during N-Back task performance. Biol Psychol. 2019;146: 107726.
Massaro S, Pecchia L. Heart rate variability (HRV) analysis: a methodology for organizational neuroscience. Organ Res Methods. 2019;22(1):354–93.
Qu H, Gao X, Pang L. Classification of mental workload based on multiple features of ECG signals. Inform Med Unlocked. 2021;24: 100575.
Smital L, et al. Real-time quality assessment of long-term ECG signals recorded by wearables in free-living conditions. IEEE Trans Biomed Eng. 2020;67(10):2721–34. https://doi.org/10.1109/TBME.2020.2969719.
Gupta K, Bajaj V. A robust framework for automated screening of diabetic patient using ecg signals. IEEE Sens J. 2022;22(24):24222–9.
Beh W-K, Yi-Hsuan W. MAUS: a dataset for mental workload assessmenton N-back task using wearable sensor. arXiv:2111.02561 2021
Gupta S, Anurag S, Abhishek S. Exploiting moving slope features of PPG derivatives for estimation of mean arterial pressure. Biomed Eng Lett. 2022;13:1–9.
Gupta S, Singh A, Sharma A. Denoising and analysis of PPG acquired from different body sites using Savitzky Golay filter. In TENCON 2022-2022 IEEE region 10 conference (TENCON). IEEE, 2022; pp. 1–4
Gupta S, Singh A, Sharma A. Denoising and analysis of PPG acquired from different body sites using Savitzky Golay filter. TENCON 2022–2022 IEEE region 10 conference (TENCON), Hong Kong, Hong Kong, 2022; pp. 1–4. https://doi.org/10.1109/TENCON55691.2022.9978083.
Plett MI. Transient detection with cross wavelet transforms and wavelet coherence. IEEE Trans Signal Process. 2007;55(5):1605–11.
Lachaux JP, Lutz A, Rudrauf D, Cosmelli D, Le Van Quyen M, Martinerie J, Varela F. Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin Clin Neurophysiol. 2002;32(3):157–74.
Gupta K, Bajaj V, Ansari IA. "Integrated S-transform-based learning system for detection of arrhythmic fetus." in IEEE Trans Instrum Meas. 2023;72:1–8. https://doi.org/10.1109/TIM.2023.3271739.
Cao J, Yan M, Jia Y, Tian X, Zhang Z. Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals. EURASIP J Adv Signal Process. 2021;2021(1):1–25.
Ekiz D, Can YS, Ersoy C. Long short-term memory network based unobtrusive workload monitoring with consumer grade smartwatches. IEEE Trans Affect Comput. 2023;14(2):895–905. https://doi.org/10.1109/TAFFC.2021.3110211.
Pan JS, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345–59.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no Conflict of interest.
Ethical approval
Not applicable
Consent to participate
Not applicable
Consent to publish
Not applicable
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gupta, S., Gupta, K. & Singh, A. Automated assessment of mental workload from PPG sensor data using cross-wavelet coherence and transfer learning. Biomed. Eng. Lett. (2024). https://doi.org/10.1007/s13534-024-00384-1
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13534-024-00384-1