Systematic Literature Review: Penggunaan Sensor dalam Deteksi Nyeri Wajah berdasarkan Database Publik
Abstract
Deteksi nyeri objektif merupakan tantangan dalam dunia medis, terutama bagi pasien yang tidak mampu mengungkapkan rasa sakit secara verbal. Dengan kemajuan teknologi sensor dan kecerdasan buatan, sistem otomatis untuk mendeteksi nyeri berbasis sinyal fisiologis dan ekspresi wajah mulai dikembangkan. Studi ini bertujuan mengidentifikasi tren, metode, dan kualitas metodologis dari penelitian yang menggunakan database publik seperti BioVid Heat Pain, UNBC-McMaster, dan SenseEmotion dalam pengembangan sistem deteksi nyeri berbasis sensor. Penelitian dilakukan dengan pendekatan Systematic Literature Review (SLR) berdasarkan protokol PRISMA 2020 melalui pencarian artikel di Google Scholar dalam rentang tahun 2015–2024. Setelah seleksi berdasarkan kriteria inklusi dan eksklusi, 26 studi dimasukkan ke dalam sintesis naratif. Data dianalisis berdasarkan jenis sensor, metode algoritma, akurasi, dan ukuran sampel, serta dievaluasi menggunakan pendekatan GRADE. Hasil menunjukkan bahwa BioVid dan UNBC-McMaster adalah database paling sering digunakan, dengan sensor EDA, EMG, dan ekspresi wajah sebagai modalitas dominan. Metode klasifikasi umum mencakup CNN, SVM, dan Random Forest. Studi menyimpulkan bahwa pendekatan multimodal dan deep learning meningkatkan akurasi deteksi nyeri, namun validasi klinis dan perhatian terhadap keragaman demografis masih dibutuhkan.
References
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[2] N. Wells, C. Pasero, and M. McCaffery, “Improving the Quality of Care Through Pain Assessment and Management.,” R. G. Hughes, Ed., Rockville (MD), 2008.
[3] S. Rejeki, Buku ajar Manajemen Nyeri (Non Farmaka) i BUKU AJAR MANAJEMEN NYERI (NON FARMAKA). 2020.
[4] M. Cascella, D. Schiavo, A. Cuomo, and ..., “Artificial intelligence for automatic pain assessment: research methods and perspectives,” Pain Research and …, 2023, doi: 10.1155/2023/6018736.
[5] F. Pouromran, S. Radhakrishnan, and S. Kamarthi, “Exploration of physiological sensors, features, and machine learning models for pain intensity estimation,” 2021, journals.plos.org. [Online]. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254108
[6] F. Pouromran, Y. Lin, and S. Kamarthi, “Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques.”
[7] P. Lucey, J. F. Cohn, K. M. Prkachin, P. E. Solomon, and I. Matthews, “Painful data: The UNBC-McMaster shoulder pain expression archive database,” 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011, pp. 57–64, 2011, doi: 10.1109/FG.2011.5771462.
[8] S. Walter et al., “The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system,” 2013 IEEE International Conference on Cybernetics (CYBCO), pp. 128–131, 2013, [Online]. Available: https://api.semanticscholar.org/CorpusID:20881798
[9] M. Velana et al., “The senseemotion database: A multimodal database for the development and systematic validation of an automatic pain-and emotion-recognition system,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10183 LNAI, pp. 127–139, 2017, doi: 10.1007/978-3-319-59259-6_11.
[10] R. Boukrab and A. Pagès-Zamora, “Random-walk Laplacian for frequency analysis in periodic graphs,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–13, 2021, doi: 10.3390/s21041275.
[11] M. Benavent-Lledo, D. Mulero-Pérez, D. Ortiz-Perez, and ..., “A comprehensive study on pain assessment from multimodal sensor data,” 2023, mdpi.com. [Online]. Available: https://www.mdpi.com/1424-8220/23/24/9675
[12] S. Gkikas and M. Tsiknakis, “Automatic assessment of pain based on deep learning methods: A systematic review.,” Comput Methods Programs Biomed, vol. 231, p. 107365, Apr. 2023, doi: 10.1016/j.cmpb.2023.107365.
[13] B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering – A systematic literature review,” Inf Softw Technol, vol. 51, no. 1, pp. 7–15, 2009, doi: https://doi.org/10.1016/j.infsof.2008.09.009.
[14] Agus Munandar, Santy Wijaya, Kemas Dedy Kusdianto, R. Dian Wirdiansyah, and Franki Slamet, “Penerapan Sistem Digitalisasi dan Kompetensi SDM Pada UMKM Systematic Literature Review,” Akuntoteknologi, vol. 15, no. 1, pp. 1–9, 2023, doi: 10.31253/aktek.v15i1.1838.
[15] “Framework Integrasi Digital Forensic Readiness dan Information Security Management System di lingkungan Pemerintahan,” 2024.
[16] Lupiyoadi, “Daftar Pustaka Daftar Pustaka,” Pemikiran Islam di Malaysia: Sejarah dan Aliran, vol. 20, no. 5, pp. 40–3, 2014, [Online]. Available: https://books.google.co.id/books?id=D9_YDwAAQBAJ&pg=PA369&lpg=PA369&dq=Prawirohardjo,+Sarwono.+2010.+Buku+Acuan+Nasional+Pelayanan+Kesehatan++Maternal+dan+Neonatal.+Jakarta+:+PT+Bina+Pustaka+Sarwono+Prawirohardjo.&source=bl&ots=riWNmMFyEq&sig=ACfU3U0HyN3I
[17] H. Schünemann, J. Brożek, G. Guyatt, and A. Oxman, “GRADE approach,” Cochrane, 2013, [Online]. Available: https://training.cochrane.org/resource/grade-handbook
[18] J. D. R. Strain, L. Welch, and E. Sadler, “Systematic review and narrative synthesis of the experiences of individuals with chronic pain participating in digital pain management interventions,” PLoS One, vol. 19, no. 7 July, pp. 1–24, 2024, doi: 10.1371/journal.pone.0306455.
[19] J. C. Ayena, A. Bouayed, M. Ben Arous, Y. Ouakrim, K. Loulou, and D. Ameyed, “Predicting chronic pain using wearable devices : a scoping review of sensor capabilities , data security , and standards compliance,” no. May, pp. 1–13, 2025, doi: 10.3389/fdgth.2025.1581285.
[20] S. Walter et al., “The biovid heat pain database: Data for the advancement and systematic validation of an automated pain recognition,” 2013 IEEE International Conference on Cybernetics, CYBCONF 2013, no. January 2015, pp. 128–131, 2013, doi: 10.1109/CYBConf.2013.6617456.
[21] K. Anja, Y. Ruijing, F. Angelos, and ..., “Pain fingerprinting using multimodal sensing: pilot study,” Multimedia Tools …, 2022, [Online]. Available: https://search.proquest.com/openview/dc5c17c746c1f4076b60becb593fad5e/1?pq-origsite=gscholar&cbl=54626
[22] E. N. Saddam, S. Mutashar, and W. H. Ali, “A study of patient’s pain assessment based on facial expression: Issues and challenges,” 2021. [Online]. Available: https://www.researchgate.net/profile/Elaf-Noori/publication/369638685_Engineering_and_Technology_Journal_A_Study_of_Patient’s_Pain_Assessment_Based_on_Facial_Expression_Issues_and_Challenges/links/64258e80a1b72772e439d197/Engineering-and-Technology-Journa
[23] R. M. Al-Eidan, H. Al-Khalifa, and A. M. Al-Salman, “Deep-learning-based models for pain recognition: A systematic review,” 2020, mdpi.com. [Online]. Available: https://www.mdpi.com/2076-3417/10/17/5984
[24] M. Kächele, P. Thiam, M. Amirian, and ..., “Methods for person-centered continuous pain intensity assessment from bio-physiological channels,” IEEE Journal of …, 2016, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7421991/
[25] R. Arora, P. Prajod, M. L. Nicora, D. Panzeri, and ..., “Socially interactive agents for robotic neurorehabilitation training: conceptualization and proof-of-concept study,” Frontiers in Artificial …, 2024, doi: 10.3389/frai.2024.1441955.
[26] B. Ozek, Z. Lu, S. Radhakrishnan, and S. Kamarthi, Study of Influence of Preexisting Pain on the Body’s Response to External Pain Stimuli. researchsquare.com, 2024. [Online]. Available: https://www.researchsquare.com/article/rs-4469036/latest
[27] A. Badura, M. Bieńkowska, A. Masłowska, and ..., “Multimodal signal acquisition for pain assessment in physiotherapy,” Information Technology …, 2021, doi: 10.1007/978-3-030-49666-1_18.
[28] S. El Morabit, New Artificial Intelligence techniques for Computer vision based medical diagnosis. theses.hal.science, 2023. [Online]. Available: https://theses.hal.science/tel-04426348/
[29] M. Cascella, D. Schiavo, A. Cuomo, and ..., “Artificial intelligence for automatic pain assessment: research methods and perspectives,” Pain Research and …, 2023, doi: 10.1155/2023/6018736.
[30] P. Gouverneur, F. Li, K. Shirahama, L. Luebke, and ..., “Explainable artificial intelligence (XAI) in pain research: Understanding the role of electrodermal activity for automated pain recognition,” 2023, mdpi.com. [Online]. Available: https://www.mdpi.com/1424-8220/23/4/1959
[31] D. Lopez-Martinez, Machine learning for pain assessment and management. dspace.mit.edu, 2020. [Online]. Available: https://dspace.mit.edu/handle/1721.1/128081
[32] F. Pouromran, Machine Learning for Automated Pain Assessment Using Physiological Signals. search.proquest.com, 2022. [Online]. Available: https://search.proquest.com/openview/5a6fece7c3c06cbcefc54dc510541d23/1?pq-origsite=gscholar&cbl=18750&diss=y
[33] M. Tavakolian, Efficient spatiotemporal representation learning for pain intensity estimation from facial expressions. oulurepo.oulu.fi, 2021. [Online]. Available: https://oulurepo.oulu.fi/handle/10024/36683
[34] P. J. Gouverneur, Machine Learning Methods for Pain Investigation Using Physiological Signals. books.google.com, 2024. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=gz4PEQAAQBAJ&oi=fnd&pg=PR3&dq=%22pain+detection%22+%22sensor%22+%22machine+learning%22+%22biovid+heat+pain+database%22&ots=_Nb00LHbkn&sig=3NmgEKi8LcuDqGhBEWMd06Zp1sU
[35] E. A. M. Othman, An automatic and multi-modal system for continuous pain intensity monitoring based on analyzing data from five sensor modalities. repo.bibliothek.uni-halle.de, 2023. [Online]. Available: https://repo.bibliothek.uni-halle.de/handle/1981185920/112357
[36] P. Werner, D. Lopez-Martinez, S. Walter, and ..., “Automatic recognition methods supporting pain assessment: A survey,” IEEE Transactions …, 2019, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8865626/
[37] P. Gouverneur, A. Badura, F. Li, M. Bieńkowska, and ..., “An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition,” 2024, nature.com. [Online]. Available: https://www.nature.com/articles/s41597-024-03878-w
[38] D. Naranjo-Hernández, J. Reina-Tosina, and L. M. Roa, “Sensor technologies to manage the physiological traits of chronic pain: a review,” 2020, mdpi.com. [Online]. Available: https://www.mdpi.com/1424-8220/20/2/365
[39] E. Othman, P. Werner, F. Saxen, M. A. Fiedler, and ..., “An automatic system for continuous pain intensity monitoring based on analyzing data from uni-, bi-, and multi-modality,” 2022, mdpi.com. [Online]. Available: https://www.mdpi.com/1424-8220/22/13/4992
[40] T. Olugbade, R. A. Buono, K. Potapov, and ..., “The EmoPain@ Home dataset: Capturing pain level and activity recognition for people with chronic pain in their homes,” IEEE Transactions …, 2024, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10504978/
[41] R. Cao, Precision Assessment and Intervention of Affective States in Next-Generation Healthcare. search.proquest.com, 2024. [Online]. Available: https://search.proquest.com/openview/a67fb28ea3931c14b57165973618198e/1?pq-origsite=gscholar&cbl=18750&diss=y
[42] A. Keskinarkaus, R. Yang, A. Fylakis, and ..., “Pain fingerprinting using multimodal sensing: pilot study,” 2022, Springer. doi: 10.1007/s11042-021-11761-8.
[43] M. R. Elassali, “Nouvelles techniques de l’intelligence artificielle pour le diagnostic médical basé sur la vision par ordinateur,” 2023, theses.fr. [Online]. Available: https://theses.fr/api/v1/document/2023UPHF0013
[44] T. Olugbade, R. A. Buono, C. D. C. Amanda, and ..., “Emopain (at) home: Dataset and automatic assessment within functional activity for chronic pain rehabilitation,” 2022 10th …, 2022, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9953831/
[45] M. Bieńkowska, A. Badura, A. Myśliwiec, and ..., “Head pose and biomedical signals analysis in pain level recognition,” … Conference on Information …, 2022, doi: 10.1007/978-3-031-09135-3_29.
Published
2025-07-21
How to Cite
Azibi, A., Hutabarat, E., Tarigan, J., Sitorus, Z., & HS, C. (2025). Systematic Literature Review: Penggunaan Sensor dalam Deteksi Nyeri Wajah berdasarkan Database Publik. Dinamik, 30(2), 389-401. https://doi.org/10.35315/dinamik.v30i2.10282
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Copyright (c) 2025 Ahmad Izzu Azibi, Emy Priyanka Hutabarat, Juan Kevin Timothi Tarigan, Zeremia Armando Sitorus, Christnatalis HS
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
