Dinamika dan arah riset affective computing dalam bidang kesehatan mental

  • Wiki Lofandri 
    Universitas Negeri Padang
    Indonesia
  • Saftrian Mukhlizul Fuad
    Universitas Negeri Padang
    Indonesia
  • Mutia Ramdhatul Jannah
    Universitas Negeri Padang
    Indonesia

Abstract

untuk mengenali dan merespons kondisi emosional manusia, dan penerapannya pada kesehatan mental menjadi arah riset yang tumbuh paling cepat dalam lima tahun terakhir. Penelitian ini memetakan struktur intelektual, tren produktivitas, dan evolusi tematik penelitian pada irisan affective computing dan kesehatan mental melalui pendekatan bibliometrik. Data diperoleh dari Scopus menggunakan boolean yang menggabungkan istilah affective computing dan emotion recognition dengan istilah kesehatan mental, dibatasi pada tahun terbit 2021–2025, jenis dokumen artikel, bahasa Inggris, jenis sumber jurnal, dan tahap publikasi final, sehingga diperoleh 828 artikel dari 424 jurnal dan lebih dari 45 negara. Analisis dilakukan menggunakan Biblioshiny (bibliometrix R) meliputi produktivitas tahunan, Hukum Bradford, Hukum Lotka, analisis sitasi, ko-kolaborasi negara, ko-occurrence kata kunci, dan pemetaan tematik. Hasil menunjukkan pertumbuhan publikasi yang meningkat tajam sejak 2023, didominasi kontribusi Tiongkok, India, dan Amerika Serikat, dengan kepenulisan yang sangat terkonsentrasi. Pemetaan ko-occurrence memperlihatkan dua klaster yang saling terhubung melalui simpul mental health, yaitu klaster teknis-komputasional (affective computing, deep learning, EEG) dan klaster klinis-demografis (emotion, depression, schizophrenia, controlled study). Penelitian ini mengidentifikasi bahwa integrasi antara pengembangan model kecerdasan buatan dan validasi klinis pada populasi khusus, terutama di kawasan berpenghasilan rendah dan menengah, masih menjadi celah penelitian yang signifikan dan menjadi agenda riset prioritas ke depan.

Keywords
  • Affective computing; Kesehatan mental; Bibliometrik; Emotion recognition; Scopus
Lisensing Information
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

References
  1. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.
  2. Awais, M., Raza, M., Singh, N., Bashir, K., Manzoor, U., Islam, S. U., & Rodrigues, J. J. P. C. (2021). LSTM-based emotion detection using physiological signals: IoT framework for healthcare and distance learning in COVID-19. IEEE Internet of Things Journal.
  3. Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86.
  4. Can, Y. S., & Ersoy, C. (2021). Privacy-preserving federated deep learning for wearable IoT-based biomedical monitoring. ACM Transactions on Internet Technology.
  5. Chen, H., Jin, M., Li, Z., Fan, C., Li, J., & He, H. (2021). MS-MDA: Multisource marginal distribution adaptation for cross-subject and cross-session EEG emotion recognition. Frontiers in Neuroscience.
  6. Dalvi, C., Rathod, M., Patil, S., Gite, S., & Kotecha, K. (2021). A survey of AI-based facial emotion recognition: Features, ML & DL techniques, age-wise datasets and future directions. IEEE Access.
  7. Denecke, K., Vaaheesan, S., & Arulnathan, A. (2021). A mental health chatbot for regulating emotions (SERMO) — concept and usability test. IEEE Transactions on Emerging Topics in Computing.
  8. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
  9. Gall, D., Roth, D., Stauffert, J.-P., Zarges, J., & Latoschik, M. E. (2021). Embodiment in virtual reality intensifies emotional responses to virtual stimuli. Frontiers in Psychology.
  10. Gedam, S., & Paul, S. (2021). A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, 9, 84045–84066.
  11. Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: An integrative review. Current Opinion in Behavioral Sciences, 18, 43–49.
  12. Han, J., Zhang, Z., Mascolo, C., André, E., Tao, J., Zhao, Z., & Schuller, B. W. (2021). Deep learning for mobile mental health: Challenges and recent advances. IEEE Signal Processing Magazine, 38(6), 96–105.
  13. Houssein, E. H., Hammad, A., & Ali, A. A. (2022). Human emotion recognition from EEG-based brain–computer interface using machine learning: A comprehensive review. Neural Computing and Applications.
  14. Lattie, E. G., Adkins, E. C., Winquist, N., Stiles-Shields, C., Wafford, Q. E., & Graham, A. K. (2019). Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: Systematic review. Journal of Medical Internet Research, 21(7), e12869.
  15. Latif, S., Rana, R., Khalifa, S., Jurdak, R., Qadir, J., & Schuller, B. W. (2021). Survey of deep representation learning for speech emotion recognition. IEEE Transactions on Affective Computing.
  16. Li, A., Jiao, D., & Zhu, T. (2018). Detecting depression stigma on social media: A linguistic analysis. Journal of Affective Disorders, 232, 358–362.
  17. Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing, 13(3), 1195–1215.
  18. Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2022). EEG based emotion recognition: A tutorial and review. ACM Computing Surveys.
  19. Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.
  20. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
  21. Philippe, T. J., Sikder, N., Jackson, A., Koblanski, M. E., Liow, E., Pilarinos, A., et al. (2022). Digital health interventions for delivery of mental health care: Systematic and comprehensive meta-review. JMIR Mental Health, 9, e35159.
  22. Picard, R. W. (1997). Affective computing. MIT Press.
  23. Picard, R. W. (2010). Affective computing: From laughter to IEEE. IEEE Transactions on Affective Computing, 1(1), 11–17.
  24. Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98–125.
  25. Salankar, N., Mishra, P., & Garg, L. (2021). Emotion recognition from EEG signals using empirical mode decomposition and second-order difference plot. Biomedical Signal Processing and Control.
  26. Schlicher, M., Li, Y., Murthy, S. M. K., Sun, Q., & Schuller, B. W. (2025). Emotionally adaptive support: A narrative review of affective computing for mental health. Frontiers in Digital Health, 7, 1657031.
  27. Schuller, B. W., Picard, R., André, E., Gratch, J., & Tao, J. (2021). Intelligent signal processing for affective computing [from the guest editors]. IEEE Signal Processing Magazine, 38(6), 9–11.
  28. Slovak, P., Antle, A., Theofanopoulou, N., Roquet, C. D., Gross, J., & Isbister, K. (2023). Designing for emotion regulation interventions: An agenda for HCI theory and research. ACM Transactions on Computer-Human Interaction.
  29. Sreehari, P., Raghavendra, U., Gudigar, A., Khan, A. R., Noronha, K., & Deka, B. K. (2025). A review of deep learning techniques for EEG-based emotion recognition: Models, methods, and datasets. F1000Research.
  30. Striegl, J., Richter, J. W., Grossmann, L., Bråstad, B., Gotthardt, M., Rück, C., et al. (2024). Deep learning-based dimensional emotion recognition for conversational agent-based cognitive behavioral therapy. PeerJ Computer Science, 10, e2104.
  31. Tao, J., & Tan, T. (2005). Affective computing: A review. In J. Tao, T. Tan, & R. W. Picard (Eds.), Affective Computing and Intelligent Interaction (LNCS Vol. 3784, pp. 981–995). Springer.
  32. Tejaswini, V., Sathya Babu, K., & Sahoo, B. (2022). Depression detection from social media text analysis using natural language processing techniques and hybrid deep learning model. ACM Transactions on Asian and Low-Resource Language Information Processing.
  33. Tremain, H., McEnery, C., Fletcher, K., & Murray, G. (2020). The therapeutic alliance in digital mental health interventions for serious mental illnesses: Narrative review. JMIR Mental Health, 7(8), e17204.
  34. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.
  35. Wang, K., et al. (2025). The global landscape of emotion recognition research from 2004 to 2023: A scientometric and visualization analysis. Discover Applied Sciences.
  36. Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. Information Fusion, 83, 19–52.
  37. Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022). Natural language processing applied to mental illness detection: A narrative review. NPJ Digital Medicine, 5(1), 1–13.
  38. Zhu, X., Wang, Y., Cambria, E., Rida, I., López, J. S., Cui, L., & Wang, R. (2025). RMER-DT: Robust multimodal emotion recognition in conversational contexts based on diversified training. Information Fusion.
  39. Zou, B., Han, J., Wang, Y., Liu, R., Zhao, S., Feng, L., Lyu, X., & Ma, H. (2023). Semi-structural interview-based Chinese multimodal depression corpus towards automated preliminary screening of depressive disorders. IEEE Transactions on Affective Computing.
  40. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472.

Downloads

PDF

Statistic

Abstract Views
: 12 times
PDF
: 6 times