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This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (1413) New Methods (1865) Tools, Programs and Algorithms (605) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (2439) Machine Learning (3329) Artificial Intelligence (4994) Perioperative Software Tools for Clinicians (32) Digital Pain Assessment and Management (326) Postoperative Pain Management (51)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67969, first published .
Doctor examining patient's knee during medical consultation

Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach

Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach

Authors of this article:

Ajan Subramanian1 Author Orcid Image ;   Rui Cao2 Author Orcid Image ;   Emad Kasaeyan Naeini1 Author Orcid Image ;   Seyed Amir Hossein Aqajari2 Author Orcid Image ;   Thomas D Hughes3 Author Orcid Image ;   Michael-David Calderon4 Author Orcid Image ;   Kai Zheng5 Author Orcid Image ;   Nikil Dutt1 Author Orcid Image ;   Pasi Liljeberg6 Author Orcid Image ;   Sanna Salanterä7, 8 Author Orcid Image ;   Ariana M Nelson9 Author Orcid Image ;   Amir M Rahmani1, 2, 3, 10 Author Orcid Image
Article Authors Cited by (16) Tweetations Metrics

Journals

  1. Defaz Defaz L, Sisalema Bonito K, Tituaña Saquinga J, Torres Iza L, Acosta Nuñez J. Use of pain assessment scales in non-communicative patients. SAP Community and Interculturality in Dialogue 2025;5:154 View
  2. Jang E, Eum Y, Yoon D, Byun S. Classifying social and physical pain from multimodal physiological signals using machine learning. Scientific Reports 2025;15(1) View
  3. Defaz Defaz L, Sisalema Bonito K, Tituaña Saquinga J, Torres Iza L, Acosta Nuñez J. Use Of Pain Assessment Scales In Non-Communicative And Communicative Patients. SAP Community and Interculturality in Dialogue 2025;5:127 View
  4. Özdemir C. Çok Modlu Biyosinyallerle Akut Ağrıların Makine Öğrenmesiyle Tespiti. Türkiye Teknoloji ve Uygulamalı Bilimler Dergisi 2025;6(2):85 View
  5. Mukherjee P, Halder Roy A, Konar A, Nagar A. EEG and EMG Induced Pain-Sensitive Learning Controller for Robotic Knee Rehabilitation Using Deep Learning. IEEE Access 2025;13:179944 View
  6. Sezerano M, Niyonkuru E. Personalized Multimodal and Opioid-Sparing Analgesia for Postoperative Pain Management: Enhancing Recovery and Addressing the Post-Discharge Gap. Journal of Pain Research 2026;Volume 19:1 View
  7. . Facial Pain and Facial Pain Assessment: Scoping Review with ☸️SAIMSARA. SAIMSARA Journal 2026;1(6) View
  8. Tsai Z, Zheng Y, Kuo C, Huang Y, Lin W, Suen M, Lin C. Personalized artificial intelligence detector for individualized pain classification using wearable heart rate data. Chinese Journal of Physics 2026;103:1634 View
  9. Yang Z, Li L, Bo S, Zhong J, Zhang Y. Current state of research and future developments of artificial intelligence in pain diagnosis and treatment. Journal of Translational Medicine 2026;24(1) View
  10. Berdida D. Who Gets Believed When Machines Read Pain? AI, Testimony and Nursing Judgement. Nursing Philosophy 2026;27(4) View
  11. Andrews J, Leslie A, Thiruvenkatarajan V, Stretton B. Integrating behavioural and physiological data: A multimodal AI framework for perioperative pain assessment. Anaesthesia and Intensive Care 2026 View
  12. Grubinskienė A, Katkevičius A. Wearable physiological sensor fusion for pain episode detection: a review. New Trends in Computer Sciences 2026;4(1):196 View

Conference Proceedings

  1. Bonilla L, Aguirre-Usandizaga J, Garcia-Perez A, Osa M, Belacortu I, Diaz-de-Arcaya J, Aroca J, Torre-Bastida A, Milioñ R, Almeida A. 2025 IEEE International Conference on Smart Computing (SMARTCOMP). Towards a Framework for Intelligent Sampling: Comprehensive Review of Challenges, AI Techniques, and Tools View
  2. Datta S, Datta G, Gedeon T, Hossain M. Companion Proceedings of the 27th International Conference on Multimodal Interaction. Painthenticate: Feature Engineering on Multimodal Physiological Signals View
  3. Grubinskienė A, Katkevičius A. 2026 IEEE 13th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). Wearable Physiological Signals for Pain Assessment and Forecasting: Cohort Design, Data Readiness, Feature Extraction, and Validation View
  4. Bibi S, Akhter J, Naseer N, Kim S, Nadeem M, Farrukh F, Nazeer H. 2026 14th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). A Feature-Based Multimodal Approach to Pain Detection and Localisation Using Wearable Biosignals View