Tinjauan Literatur Sistematis Deteksi Nyeri Objektif Berbasis Aktivitas Elektrodermal: Tantangan Teknis dan  Peluang Implementasi

Andrian Reinaldo Crispin; Felix Matthew Jonathan

doi:10.30865/json.v8i1.9862

Authors

Andrian Reinaldo Crispin
Felix Matthew Jonathan Universitas Prima Indonesia

DOI:

https://doi.org/10.30865/json.v8i1.9862

Keywords:

Aktivitas Elektrodermal (EDA), Deteksi Nyeri, Artefak Gerakan, Machine Learning, Explainable AI(XAI)

Abstract

Pengukuran intensitas nyeri di lingkungan klinis saat ini sebagian besar masih bersifat subjektif dan mengandalkan pelaporan mandiri pasien, sehingga berisiko memicu bias evaluasi pada populasi non-verbal. Sebagai solusi objektif, parameter otonom Electrodermal Activity (EDA) atau konduktansi kulit mulai banyak dieksplorasi karena keterkaitannya yang erat dengan aktivitas sistem saraf simpatis. Penelitian ini bertujuan untuk mengidentifikasi, menganalisis, dan menyintesis tantangan teknis serta peluang implementasi sistem deteksi nyeri objektif berbasis EDA melalui metode Systematic Literature Review (SLR) dengan mengadopsi pedoman PRISMA 2020. Penapisan literatur dilakukan secara ketat terhadap artikel-artikel orisinal bereputasi internasional dalam rentang waktu terupdate. Hasil sintesis data menunjukkan bahwa tantangan teknis utama dalam implementasi EDA klinis meliputi kerentanan yang tinggi terhadap artefak gerakan (motion artifacts), variabilitas kondisi biologis kulit individu, serta adanya disparitas regulasi otonom yang dipengaruhi oleh faktor gender (female-male differences). Di sisi lain, peluang keberhasilan teknologi ini didukung oleh perkembangan algoritma pemrosesan sinyal digital modern, seperti pemanfaatan fitur diferensial (dphEDA dan MTVSymp), serta implementasi model supervised maupun unsupervised machine learning untuk otomatisasi deteksi dan eliminasi artefak gerakan. Lebih lanjut, integrasi arsitektur deep learning kontinu (seperti CrossMod-Transformer) dan penerapan kerangka kerja Explainable Artificial Intelligence (XAI) berpotensi meminimalisasi keterbatasan sensor tunggal sekaligus memberikan transparansi visual atas keputusan klinis model cerdas pada populasi anak-anak, dental, maupun ginekologi. Penelitian ini diharapkan dapat memberikan landasan teoritis bagi perancangan aplikasi smart healthcare masa depan berbasis Edge AI pada perangkat wearable.

References

[1] Y. Kong, H. F. Posada-Quintero, and K. H. Chon, “Multi-level Pain Quantification using a Smartphone and Electrodermal Activity,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 2475–2478. doi: 10.1109/EMBC48229.2022.9871228.

[2] J. Chen, M. Abbod, and J. S. Shieh, “Pain and stress detection using wearable sensors and devices—a review,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–18, Feb. 2021, doi: 10.3390/s21041030.

[3] B. Susam et al., “Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning,” IEEE Trans. Biomed. Eng., vol. 69, no. 1, pp. 422–431, Jan. 2022, doi: 10.1109/TBME.2021.3096137.

[4] H. Aloulou, B. Abdulrazak, A. de Marassé-Enouf, and M. Mokhtari, Eds., Lecture Notes in Computer Science, vol. 13287. in Lecture Notes in Computer Science, vol. 13287. Cham: Springer International Publishing, 2022. doi: 10.1007/978-3-031-09593-1.

[5] R. McNaboe et al., “Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity,” Sensors, vol. 22, no. 22, Nov. 2022, doi: 10.3390/s22228851.

[6] Y. Kong, H. F. Posada-Quintero, and K. H. Chon, “Real-time high-level acute pain detection using a smartphone and a wrist-worn electrodermal activity sensor,” Sensors, vol. 21, no. 12, p. 3956, Jun. 2021, doi: 10.3390/s21123956.

[7] J. Farmani, G. Bargshady, S. Gkikas, M. Tsiknakis, and R. F. Rojas, “A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion,” Sci. Rep., vol. 15, no. 1, p. 14238, Dec. 2025, doi: 10.1038/s41598-025-14238-y.

[8] H. T. Tran, Y. Kong, A. Talati, H. Posada-Quintero, K. H. Chon, and I. P. Chen, “The use of electrodermal activity in pulpal diagnosis and dental pain assessment,” Int. Endod. J., vol. 56, no. 3, pp. 356–368, Mar. 2023, doi: 10.1111/iej.13868.

[9] M. B. Hossain, H. F. Posada-Quintero, Y. Kong, R. McNaboe, and K. H. Chon, “Automatic motion artifact detection in electrodermal activity data using machine learning,” Biomed. Signal Process. Control, vol. 74, p. 103483, Apr. 2022, doi: 10.1016/j.bspc.2022.103483.

[10] Y. Kong, H. F. Posada-Quintero, and K. H. Chon, “Female-male Differences Should be Considered in Physical Pain Quantification based on Electrodermal Activity: Preliminary Study,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 6941–6944. doi: 10.1109/EMBC46164.2021.9630637.

[11] C. Joseph, M. Ghahramani, and R. F. Rojas, “Real-Time Pain Assessment from Electrodermal Activity Using Deep Learning,” Sensors, vol. 26, no. 10, p. 3020, May 2026, doi: 10.3390/s26103020.

[12] M. B. Hossain, H. F. Posada-Quintero, Y. Kong, R. McNaboe, and K. H. Chon, “A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 6920–6923. doi: 10.1109/EMBC46164.2021.9629513.

[13] R. Fernandez Rojas et al., “Multimodal physiological sensing for the assessment of acute pain,” Frontiers in Pain Research, vol. 4, p. 1150264, 2023, doi: 10.3389/fpain.2023.1150264.

[14] P. Gouverneur et al., “Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition,” Sensors, vol. 23, no. 4, p. 1959, Feb. 2023, doi: 10.3390/s23041959.

[15] S. Subramanian, B. Tseng, R. Barbieri, and E. N. Brown, “Unsupervised Machine Learning Methods for Artifact Removal in Electrodermal Activity,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 399–402. doi: 10.1109/EMBC46164.2021.9630535.

[16] H. Hirayama, S. Yoshida, K. Sasaki, E. Yuda, Y. Yoshida, and M. Miyashita, “Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females,” BMC Res. Notes, vol. 18, no. 1, Dec. 2025, doi: 10.1186/s13104-025-07098-2.

[17] M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” Mar. 29, 2021, BMJ Publishing Group. doi: 10.1136/bmj.n71.

[18] Y. Kong, H. F. Posada-Quintero, and K. H. Chon, “Sensitive Physiological Indices of Pain Based on Differential Characteristics of Electrodermal Activity,” IEEE Trans. Biomed. Eng., vol. 68, no. 10, pp. 3122–3130, Oct. 2021, doi: 10.1109/TBME.2021.3065218.

[19] V. Bhatkar, R. Picard, and C. Staahl, “Combining Electrodermal Activity With the Peak-Pain Time to Quantify Three Temporal Regions of Pain Experience,” Front. Pain Res., vol. 3, p. 764128, 2022, doi: 10.3389/fpain.2022.764128.

[20] Y. Li et al., “Children’s Pain Identification Based on Skin Potential Signal,” Sensors, vol. 23, no. 15, Aug. 2023, doi: 10.3390/s23156815.

[21] F. R. Avila et al., “Wearable electronic devices for chronic pain intensity assessment: A systematic review,” Pain Pract., vol. 21, no. 8, pp. 955–965, Nov. 2021, doi: 10.1111/papr.13047.

[22] M. Trabelsi, M. Khan, Z. Bouida, and M. Murugappan, Eds., Proceedings of the 2nd Symposium on Smart, Sustainable, and Secure Internet of Things, vol. 1513. in Lecture Notes in Electrical Engineering, vol. 1513. Singapore: Springer, 2026. doi: 10.1007/978-981-95-5136-1.