Skip to main content

Advertisement

MV-DUO: multi-variate discrete unified optimization for psychological vital assessments

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Psychological vital assessments are required for monitoring health conditions and observing body reactions toward diseases and medications. Wearable sensors play a vital role in sensing body vitals and presenting them as signals for computer-based analysis. The problem relies on the signal decoding due to its input stream that turns out to be discrete/ continuous. Therefore for addressing the above specific issue, this article introduces MV-DUO (Multi-Variate Discrete Unified Optimization) method. This method addresses the above problem from a multi-variate perspective by sensing differential signals across healthy and unhealthy conditions. The healthy and unhealthy conditions are trained using neural learning by augmenting/ ceasing external vital data. The unification is performed using single-point artificial ecosystem-based optimization for identifying discrete sequences collaborated with continuous signals. The single-point reference is grouped based on the maximum continuity fitness observed under various sensing intervals. In this process, the non-grouped sequences are identified as unhealthy or discrete for which additional detection training and classification are required. Considerably the changes between successive sensing intervals are used for variations detection from unified high-fitness groups. Those grouped instances are used for training new vital changes observed at distinct intervals. This improves detection accuracy under controlled errors. For the varying sensing intervals, the proposed method achieves 14.13% high accuracy, 8.29% high grouping rate, 10.77% less error, and 10.07% less detection time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Ding S, Ke Z, Yue Z, Song C, Lu L (2022) Noncontact multiphysiological signals estimation via visible and infrared facial features fusion. IEEE Trans Instrum Meas 71:1–13

    Google Scholar 

  2. Tara K, Islam MH (2020) Advances of cardiac state-inducing prototype and design of GUI to anatomize cardiac signal for ascertaining psychological working competence. Sensing and Bio-Sensing Research 30:100376

    Article  Google Scholar 

  3. Wang Z, Li J, Jin Y, Wang J, Yang F, Li G, Ding W (2021) Sensing beyond itself: Multi-functional use of ubiquitous signals towards wearable applications. Digital Signal Proce 116:103091

    Article  Google Scholar 

  4. van Lier HG, Pieterse ME, Garde A, Postel MG, de Haan HA, Vollenbroek-Hutten MM, Noordzij ML (2020) A standardized validity assessment protocol for physiological signals from wearable technology: Methodological underpinnings and an application to the E4 biosensor. Behav Res Methods 52:607–629

    Article  Google Scholar 

  5. Ali F, El-Sappagh S, Islam SR, Ali A, Attique M, Imran M, Kwak KS (2021) An intelligent healthcare monitoring framework using wearable sensors and social networking data. Futur Gener Comput Syst 114:23–43

    Article  Google Scholar 

  6. Stojchevska M, Steenwinckel B, Van Der Donckt J, De Brouwer M, Goris A, De Turck F, Ongenae F (2022) Assessing the added value of context during stress detection from wearable data. BMC Med Inform Decis Mak 22(1):268

    Article  Google Scholar 

  7. Hilty DM, Armstrong CM, Edwards-Stewart A, Gentry MT, Luxton DD, Krupinski EA (2021) Sensor, wearable, and remote patient monitoring competencies for clinical care and training: scoping review. J Techno in Behav Sci 6:252–277

    Article  Google Scholar 

  8. Rylo MN, de Medeiros RL, de Lucena Jr VF (2022) Gesture recognition of wrist motion based on wearables sensors. Proce Com Sci 210:181–188

    Article  Google Scholar 

  9. Oyekan J, Chen Y, Turner C, Tiwari A (2021) Applying a fusion of wearable sensors and a cognitive inspired architecture to real-time ergonomics analysis of manual assembly tasks. J Manuf Syst 61:391–405

    Article  Google Scholar 

  10. Matsuno A, Matsushima A, Saito M, Sakurai K, Kobayashi K, Sekijima Y (2023) Quantitative assessment of the gait improvement effect of LSVT BIG® using a wearable sensor in patients with Parkinson’s disease. Heliyon. 9(6):e16952

    Article  Google Scholar 

  11. Pierleoni P, Belli A, Concetti R, Palma L, Pinti F, Raggiunto S, Monteriù A (2021) Biological age estimation using an eHealth system based on wearable sensors. J Ambi Intell Hum Com 12:4449–4460

    Article  Google Scholar 

  12. Mehmood I, Li H, Qarout Y, Umer W, Anwer S, Wu H, Antwi-Afari MF (2023) Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data. Adv Eng Inform 56:101978

    Article  Google Scholar 

  13. Dev A, Roy N, Islam MK, Biswas C, Ahmed HU, Amin MA, Mamun KA (2022) Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review. IEEE Access 10:16756–16781

    Article  Google Scholar 

  14. Mann LM, Walker BR (2022) The role of equanimity in mediating the relationship between psychological distress and social isolation during COVID-19. J Affect Disord 296:370–379

    Article  Google Scholar 

  15. Aktaş A, Uğur Ö (2023) The effect of physical and psychological symptoms on spiritual well-being and emotional distress in inpatient cancer patients. Support Care Cancer 41(6):986

    Google Scholar 

  16. Maffly-Kipp J, Gause C, Kim J, Vess M, Hicks JA (2022) Agentic meaning-making: Free will beliefs, sense-making, and psychological distress following collective traumas. Curr Rese Ecolo Soc Psy 3:100074

    Google Scholar 

  17. Ma X, Foong LK, Morasaei A, Ghabussi A, Lyu Z (2020) Swarm-based hybridizations of neural network for predicting the concrete strength. Smart Struct Syst 26(2):241–251. https://doi.org/10.12989/SSS.2020.26.2.241

    Article  Google Scholar 

  18. Adom D, Mensah JA, Osei M (2021) The psychological distress and mental health disorders from COVID-19 stigmatization in Ghana. Social sciences & humanities open 4(1):100186

    Article  Google Scholar 

  19. Jiang S, Firouzi F, Chakrabarty K, Elbogen EB (2021) A resilient and hierarchical IoT-based solution for stress monitoring in everyday settings. IEEE Internet Things J 9(12):10224–10243

    Article  Google Scholar 

  20. Ahmed U, Lin JCW, Srivastava G (2022) Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection. IEEE J Biomed Health Inform 27(2):768–777

    Article  Google Scholar 

  21. Huang W, Wu W, Lucas MV, Huang H, Wen Z, Li Y (2021) Neurofeedback training with an electroencephalogram-based brain-computer interface enhances emotion regulation. IEEE Trans Affect Comput 4(2):998–1011

    Article  Google Scholar 

  22. Attar ET, Balasubramanian V, Subasi E, Kaya M (2021) Stress analysis based on simultaneous heart rate variability and EEG monitoring. IEEE Journal of Translational Engineering in Health and Medicine 9:1–7

    Article  Google Scholar 

  23. Han H (2023) Fuzzy clustering algorithm for university students’ psychological fitness and performance detection. Heliyon. 9(8):e18550

    Article  Google Scholar 

  24. Brown SL, Roush JF, Marshall AJ, Jones C, Key C (2020) The intervening roles of psychological inflexibility and functional impairment in the relation between cancer-related pain and psychological distress. Int J Behav Med 27:100–107

    Article  Google Scholar 

  25. Wolfson PE, Andries J, Feduccia AA, Jerome L, Wang JB, Williams E, Doblin R (2020) MDMA-assisted psychotherapy for treatment of anxiety and other psychological distress related to life-threatening illnesses: a randomized pilot study. Sci Rep 10(1):20442

    Article  Google Scholar 

  26. Cheema A, Singh M, Kumar M, Setia G (2023) Combined empirical mode decomposition and phase space reconstruction based psychologically stressed and non-stressed state classification from cardiac sound signals. Biomed Signal Process Control 82:104585

    Article  Google Scholar 

  27. Arslan G, Genç E, Yıldırım M, Tanhan A, Allen KA (2022) Psychological maltreatment, meaning in life, emotions, and psychological health in young adults: A multi-mediation approach. Child Youth Serv Rev 132:106296

    Article  Google Scholar 

  28. Hong JH, Lachman ME, Charles ST, Chen Y, Wilson CL, Nakamura JS, Kim ES (2021) The positive influence of sense of control on physical, behavioral, and psychosocial health in older adults: An outcome-wide approach. Prev Med 149:106612

    Article  Google Scholar 

  29. Elzeiny S, Qaraqe M (2021) Automatic and intelligent stressor identification based on photoplethysmography analysis. IEEE Access 9:68498–68510

    Article  Google Scholar 

  30. Arora A, Chakraborty P, Bhatia MPS (2020) Analysis of data from wearable sensors for sleep quality estimation and prediction using deep learning. Arab J Sci Eng 45:10793–10812

    Article  Google Scholar 

  31. Jin J, Gao B, Yang S, Zhao B, Luo L, Woo WL (2020) Attention-block deep learning based features fusion in wearable social sensor for mental wellbeing evaluations. Ieee Access 8:89258–89268

    Article  Google Scholar 

  32. Zhu L, Spachos P, Ng PC, Yu Y, Wang Y, Plataniotis K, Hatzinakos D (2023) Stress Detection Through Wrist-Based Electrodermal Activity Monitoring and Machine Learning. IEEE J Biomed Health Inform 27(5):2155–2165

    Article  Google Scholar 

  33. Chen CM, Anastasova S, Zhang K, Rosa BG, Lo BP, Assender HE, Yang GZ (2019) Towards wearable and flexible sensors and circuits integration for stress monitoring. IEEE J Biomed Health Inform 24(8):2208–2215

    Article  Google Scholar 

  34. Cosoli G, Poli A, Scalise L, Spinsante S (2021) Measurement of multimodal physiological signals for stimulation detection by wearable devices. Measurement 184:109966

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank BIOCORE Research Group, Center for Advanced Computing Technology (C-ACT), Fakulti Teknologi Maklumat dan Komunikasi (FTMK) and Centre for Research and Innovation Management (CRIM), Universiti Teknikal Malaysia Melaka (UTeM) for providing the facilities and support for this research activities.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Shakeel Pethuraj.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pethuraj, M.S., Burhanuddin, M.A. & Dzakiyullah, N.R. MV-DUO: multi-variate discrete unified optimization for psychological vital assessments. Neural Comput & Applic 36, 19777–19793 (2024). https://doi.org/10.1007/s00521-024-10183-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-024-10183-5

Keywords