Intrinsic Motivational States Can Be Classified by Non-Contact Measurement of Autonomic Nervous System Activation and Facial Expressions
Source abstract
Abstract
Motivation is a primary driver of goal-directed behavior. Therefore, the development of cost-effective and easily applicable systems to objectively quantify motivational states is needed. To achieve our goal, this study investigated the feasibility of classifying high- and low-motivation states by machine learning based on a diversity of features obtained by non-contact measurement of physiological responses and facial expression analysis. A random forest classifier with feature selection yielded modest success in the classification of high- and low-motivation states. Further analysis linked high-motivation states to the indices of autonomic nervous system activation reflective of reduced sympathetic activation and stronger, more intense expressions of happiness. The performance of motivational state classification systems should be further improved by incorporating different varieties of non-contact measurements.
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Research context
OpenAlex topics: Heart Rate Variability and Autonomic Control · Emotion and Mood Recognition · EEG and Brain-Computer Interfaces
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Advanced Integration Science, Chiba University, Chiba 263-8522, Japan
Faculty of Computer Science, University of Information Technology, Vietnam National University, Ho Chi Minh City 71308, Vietnam
Department of Information and Management Systems Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka 940-2188, Japan
Data Science and AI Innovation Research Promotion Center, Shiga University, 1-1-1 Baba, Hikone 522-8522, Japan
School of Science and Engineering, Kokushikan University, 4-28-1 Setagaya, Tokyo 154-8515, Japan
Hiroshima University Hospital, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
Funding
Japan Science and Technology Agency (JST)
References (57)
Show source-provided references
1. Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions10.1006/ceps.1999.1020 ↗
2. Manipulatory motivation in the infant rhesus monkey10.1037/h0047817 ↗
3. The structure of intrinsic motivation10.1146/annurev-orgpsych-012420-091122 ↗
4. A self-determination theory approach to predicting school achievement over time: The unique role of intrinsic motivation10.1016/j.cedpsych.2014.08.002 ↗
5. Student Motivation and Associated Outcomes: A Meta-Analysis From Self-Determination Theory10.1177/1745691620966789 ↗
6. The Work Preference Inventory: Assessing intrinsic and extrinsic moti-vational orientations10.1037/0022-3514.66.5.950 ↗
7. Csikszentmihályi, M. (1990). The Domain of Creativity. Theories of Creativity190–212, Sage.
8. Undermining children’s intrinsic interest with extrinsic reward: A test of the “overjus-tification” hypothesis10.1037/h0035519 ↗
9. Neural basis of the undermining effect of monetary reward on intrinsic motivation10.1073/pnas.1013305107 ↗
10. The undermining effect is a reality after all—Extrinsic rewards, task interest, and self-determination: Reply to Eisenberger, Pierce, and Cameron (1999) and Lepper, Henderlong, and Gingras (1999)10.1037/0033-2909.125.6.692 ↗
11. Impaired prefrontal activity to regulate the intrinsic motivation-action link in schizophrenia10.1016/j.nicl.2017.07.003 ↗
12. The dark side of monetary incentive: How does extrinsic reward crowd out intrinsic motivation10.1097/WNR.0000000000000113 ↗
13. Neural Basis of Intrinsic Motivation: Evidence from Event-Related Potentials10.1155/2015/698725 ↗
14. Barry, P., Goettl, H.M., Halff, C.L.R., and Valerie, J.S. (1998, January 16–19). Motivation diagnosis in intelligent tutoring systems. Proceedings of the Fourth International Conference on ITS, San Antonio, TX, USA.10.1007/3-540-68716-5 ↗
15. De Vicente, A., and Pain, H. (2002). Informing the detection of the students’ motivational state: An empirical study. International Conference on Intelligent Tutoring Systems, Springer.10.1007/3-540-47987-2_93 ↗
16. Student Behavior and Interaction Patterns With an LMS as Motivation Predictors in E-Learning Settings10.1109/TE.2009.2027433 ↗
17. Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network10.1016/j.eswa.2021.115548 ↗
18. Independence and bipolarity in the structure of current affect10.1037/0022-3514.74.4.967 ↗
19. Nasoz, F., Alvarez, K., Christine, L., and Finkelstein, N. (2003, January 22–26). Emotion Recognition from Physiological Signals for User Modeling of Affect. Proceedings of the 9th International Conference on User Mode, Pittsburg, PA, USA.
20. Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability10.1109/TAFFC.2015.2432810 ↗
21. Lane, R.D., and Nadel, L. (2000). Measuring emotion: Behavior, feeling, and physiology. Cognitive Neuroscience of Emotion, Oxford University Press.
22. Kurita, K., Yonezawa, T., Kuroshima, M., and Tsumura, N. (2015, January 19–23). Non-Contact Video Based Estimation for Heart Rate Variability Spectrogram using Ambient Light by Extracting Hemoglobin Information. Proceedings of the Color and Imaging Conference, Darmstadt, Germany.10.2352/CIC.2015.23.1.art00037 ↗
23. A survey on ECG analysis10.1016/j.bspc.2018.03.003 ↗
24. Malac, A.A., Saiful, I., Saad, A.A., and Ahmed, S.B. (2022). Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review. Healthcare, 10.10.3390/healthcare10030547 ↗
25. Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging
26. Shahid, H., Butt, A., Aziz, S., Khan, M.U., and Naqvi, S.Z.H. (2020, January 16–17). Emotion Recognition System featuring a fusion of Electrocardiogram and Photoplethysmogram Features. Proceedings of the 14th International Conference on Open Source Systems and Technologies, Lahore, Pakistan.10.1109/ICOSST51357.2020.9333021 ↗
27. Lee, M.S., Lee, Y.K., Pae, D.S., Lim, M.T., Kim, D.W., and Kang, T.K. (2019). Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network. Appl. Sci., 9.10.3390/app9163355 ↗
28. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation10.1364/OE.18.010762 ↗
29. Okubo, T., Ono, K., and Tsumura, N. (2023, January 16). Improving driving ability using biofeedback by monitoring the mental situation by RGB camera. Proceedings of the SPIE BiOS, San Francisco, CA, USA.10.1117/12.2648008 ↗
30. Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin10.1145/882262.882344 ↗
31. Improved analysis for skin color separation based on independent component analysis10.1007/s10015-019-00572-8 ↗
32. Motivational determinants of risk-taking behavior10.1037/h0043445 ↗
33. Distance-PPG: Robust Non-Contact Vital Signs Monitoring Using a Camera10.1364/BOE.6.001565 ↗
34. An advanced detrending method with application to HRV analysis10.1109/10.979357 ↗
35. Shaffer, F., and Ginsberg, J.P. (2017). An Overview of Heart Rate Variability Metrics and Norms. Front. Public Health, 5.10.3389/fpubh.2017.00258 ↗
36. Ekman, P., and Friesen, W.V. (1978). Facial Action Coding System: A Technique for the Measurement of Facial Movement, Consulting Psychologists Press.10.1037/t27734-000 ↗
37. Cohn, J.F., Ambadar, Z., and Ekman, P. (2007). Handbook of Emotion Elicitation and Assessment, Oxford University Press.
38. Baltrusaitis, T., Robinson, P., and Morency, L.P. (2016, January 7–10). OpenFace: An open source facial behavior analysis toolkit. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Lake Placid, NY, USA.10.1109/WACV.2016.7477553 ↗
39. Constants across cultures in the face and emotion10.1037/h0030377 ↗
40. Ekman, P., Friesen, W.V., and Hager, J.C. (2002). Facial Action Coding System. Manual and Investigator’s Guide, Consulting Psychologists Press.
41. (2024, June 27). scikit-learn 1.3.0 documentation. Sequential Feature Selector. Available online: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html.
42. (2024, June 27). scikit-learn 1.3.0 documentation. Recursive Feature Elimination. Available online: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html.
43. (2024, June 27). sklearn.linear_model.LogisticRegression. Available online: https://scikit-learn.org/1.3/modules/generated/sklearn.linear_model.LogisticRegression.html.
44. (2024, June 27). sklearn.ensemble.RandomForestClassifier. Available online: https://scikit-learn.org/1.3/modules/generated/sklearn.ensemble.RandomForestClassifier.html.
45. (2024, June 27). sklearn.svm.SVC. Available online: https://scikit-learn.org/1.3/modules/generated/sklearn.svm.SVC.html.
46. Beat to beat variability in cardiovascular variables: Noise or music?10.1016/0735-1097(89)90408-7 ↗
47. Heart rate variability: Standards of measurement, Physiological Interpretation, and Clinical Use10.1093/oxfordjournals.eurheartj.a014868 ↗
48. Heart Rate Variability for Evaluating Psychological Stress Changes in Healthy Adults: A Scoping Review10.1159/000530376 ↗
49. The catecholamine hypothesis of affective disorders: A review of supporting evidence10.1176/ajp.122.5.509 ↗
50. The Emerging Neuroscience of Intrinsic Motivation: A New Frontier in Self-Determination Research
51. Relating Specific Emotions to Intrinsic Motivation: On the Moderating Role of Positive and Negative Emotion Differentiation10.1371/journal.pone.0115396 ↗
52. Optimal experience in work and leisure10.1037/0022-3514.56.5.815 ↗
53. Ryan, R.M. (2019). Flow: The experience of intrinsic motivation. The Oxford Handbook of Human Motivation, Oxford University Press. [2nd ed.].10.1093/oxfordhb/9780190666453.001.0001 ↗
54. Smiling Signals Intrinsic Motivation10.1093/jcr/ucz023 ↗
55. Ewa, M.E., Daniel, M., and Ashok, V. (2020, January 14–19). A Meta-Analysis of the Impact of Skin Type and Gender on Non-contact Photoplethysmog-raphy Measurements. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.
56. Goodfellow, I.J., Shlens, J., and Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv.
57. Baia, A.E., Biondi, G., Franzoni, V., Milani, A., and Poggioni, V. (2022). Lie to Me: Shield Your Emotions from Prying Software. Sensors, 22.10.3390/s22030967 ↗