Pain Assessment: A Proof of Concept for the Correlation of Sensor-based Physiological Readings to Self-Report Methods


  • Muhammad Irsyad Sabiq Ismail School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia
  • Nurul Hashimah Ahamed Hassain Malim School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia
  • Asrulnizam Abd Manaf Collaborative Microelectronic Design Excellence Centre, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Khairu Anuar Mohamed Zain Collaborative Microelectronic Design Excellence Centre, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Dzul Azri Mohamed Noor School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia


Correlation between Self-Report and Physiological Measurements, Pain Score, Physiological Measurement, Self-Report,


Pain can cause emotional effects on human-like anger, depression, mood swings, and irritability. The discomfort caused by pain can only be seen, but the level of the pain is only felt by the person enduring the pain. One method used by clinicians and doctors to identify one’s pain level is the use of pain score to rate the level of pain endured. Three ways are available to rate the level of pain, which are the patient’s selfreport method, behavioral measurement, and physiological measurement. This study focuses on the correlation between two methods, which are physiological measurement and the selfreport method. The hybrid of integrated physiological sensors and self-report mobile applications is used for system testing in this study. Three physiological variables were used to be collected in system testing which are the heart rate, body temperature, and Galvanic Skin Response. While for the selfreport, an Android mobile application was used to capture the pain level experienced by the authors in the form of numerical scale. To find the correlation between them, all the data collected from the system testing were analyzed using Pearson correlation coefficient formula. The results of the correlation suggested that the heart rate and GSR has a positive relationship with the selfreport, while body temperature has a non-correlated hypothesis. For further work, medical science people and clearance from human ethics need to be considered in the assessment.


M. Eriksson, H. Storm, A. Fremming, & J.Schollin, “Skin conductance compared to a combined behavioural and physiological pain measure in newborn infants,” Acta Paediatrica, vol. 97, no. 1, 2011, pp. 27-30.

D. D. Price, P. A. Mcgrath, A. Rafii, and B. Buckingham, “The validation of visual analogue scales as ratio scale measures for chronic and experimental pain,” Pain, vol. 17, no. 1, 1983, pp. 45–56.

“Pain Community Centre,” Pain Assessment Tools | Pain Community Centre. [Online]. Available: [Accessed: 01-Oct-2017].

M. W. Heft and S. R. Parker, “An experimental basis for revising the graphic rating scale for pain,” Pain, vol. 19, no. 2, 1984, pp. 153–161.

C. McLeod, “Don’t Forget the Bubbles,” Self-reported pain scales, 2018.

M. Helfand, M. Freeman, “Assessment and management of acute pain in adult medical inpatients: a systematic review,” Pain Medicine, vol 10, 2009, pp. 1183–99.

R. Cowen, M. K. Stasiowska, H. Laycock, and C. Bantel, “Assessing pain objectively: the use of physiological markers,” Anaesthesia, vol. 70, no. 7, 2015, pp. 828–847.

M. Arif-Rahu and M. J. Grap, “Facial expression and pain in the critically ill non-communicative patient: State of science review,” Intensive and Critical Care Nursing, vol. 26, no. 6, 2010, pp. 343–352.

D. Bieri, R. A. Reeve, D. G. Champion, L. Addicoat, and J. B. Ziegler, “The faces pain scale for the self-assessment of the severity of pain experienced by children: Development, initial validation, and preliminary investigation for ratio scale properties,” Pain, vol. 41, no. 2, 1990, pp. 139–150.

P. A. Mcgrath,“An assessment of children’s pain: a review of behavioral, physiological and direct scaling techniques,” Pain, vol. 31, no 2, pp. 147-176, 1987.

M. R. Yuce, P. C. Ng, & J.Y. Khan, “Monitoring of Physiological Parameters from Multiple Patients Using Wireless Sensor Network,” Journal of Medical Systems, vol. 32, no. 5, pp. 433-441, 2008.

J. N. Stinson, L.A. Jibb, C. Nguyen, P.C. Nathan, A.M. Maloney, L. Dupuis, M. Orr, “Development and Testing of a Multidimensional iPhone Pain Assessment Application for Adolescents with Cancer,” Journal of Medical Internet Research, vol. 15, no. 3, 2013.

U. Bengtsson, K. Kjellgren, S. Höfer, C. Taft & L. Ring, “Developing an interactive mobile phone self-report system for self-management of hypertension. Part 2: Content validity and usability,” Blood Pressure, vol. 23, no. 5, pp. 296-306, 2014.

M. Jensen, et al. “Studies Comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for Assessment of Pain Intensity in Adults: A Systematic Literature Review.” Journal of Pain and Symptom Management, vol. 41, no. 6, 2011, pp. 1073–1093.

Y. Tousignant-Laflamme, P. Rainville & S. Marchand, “Establishing a Link Between Heart Rate and Pain in Healthy Subjects: A Gender Effect,” The Journal of Pain, vol. 6, no. 6, pp. 341-347, 2005.

L. Tripathi & P. Kumar, “Challenges in pain assessment: Pain intensity scales. Indian Journal of Pain,” vol. 28, no. 2, pp. 61, 2014.

M. P. Jensen, P. Karoly, “Self-report scales and procedures for assessing paininadults,” Handbookofpainassessment, vol. 2, pp. 15-34, 2001.

L. Gagliese, “Assessment of pain in elderly people,” Handbook of pain assessment, vol. 7, pp.119-133, 2001.

B. S. Krauss, L. Calligaris, S. M. Green, and E. Barbi, “Current concepts in management of pain in children in the emergency department,” The Lancet, vol. 387, no. 10013, 2016, pp. 83–92.

D.Wong and C. Baker, “Pain in children: comparison of assessment scales,” Pediatric Nursing, Vol.14, no 1, 1988, pp. 9-17.

S. W. Krechel & J. Bildner, “CRIES: a new neonatal postoperative pain measurement score, “Initial testing of validity and reliability,” Pediatric Anesthesia, vol 5, no 1, pp. 53-61, 1995.

H. Storm, “Skin conductance and the stress response from heel stick in preterm infants,” Archives of Disease in Childhood - Fetal and Neonatal Edition, vol. 83, no. 2, 2000.

N. Nourbakhsh, Y. Wang, F. Chen & R. A. Calvo, “Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks,” Proceedings of the 24th Australian Computer-Human Interaction Conference on - OzCHI 12, 2012.

J. Younger, R. Mccue & S. Mackey, “Pain outcomes: A brief review of instruments and techniques,” Current Pain and Headache Reports, vol. 13, no. 1, pp. 39-43, 2009.

Can We Measure Pain Intensity? - Revista Mètode. (n.d.). Retrieved from

P. Lucey, J.F. Cohn, I. Matthews, S. Lucey, S. Sridharan, J. Howlett & K. M. Prkachin, “Automatically Detecting Pain in Video Through Facial Action Units”. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol 41, no 3, pp. 664-674, 2011.

A. M. Harvey, “Classification of Chronic Pain—Descriptions of Chronic Pain Syndromes and Definitions of Pain Terms,” The Clinical Journal of Pain, vol. 11, no 2, pp. 163, 1995.

N. Phan, C. Blome, F. Fritz, J. Gerss, A. Reich, T. Ebata, M. Augustin, J. Szepietowski and S. Ständer, “Assessment of Pruritus Intensity: Prospective Study on Validity and Reliability of the Visual Analogue Scale,” Numerical Rating Scale and Verbal Rating Scale in 471 Patients with Chronic Pruritus. Acta Dermato Venereologica, vol 92, no. 5, pp.502-507, 2012.

P. E. Bijur, “Validation of a Verbally Administered Numerical Rating Scale of Acute Pain for Use in the Emergency Department,” Academic Emergency Medicine, vol. 10, no. 4, pp. 390-392, 2003.

Brunelli, Cinzia & Zecca et al., “Comparison of numerical and verbal rating scales to measure pain exacerbations in patients with chronic cancer pain,” Health and quality of life outcomes, 2010.

H. Breivik, P. C. Borchgrevink, S.M., Allen, L. Rosseland, L. A., L. Romundstad, E. Hals, A Stubhaug, “Assessment of pain,” British Journal of Anaesthesia, vol 101, no 1, 2008, pp 17-24.

C. R. Chapman, K. L. Casey, R. Dubner, K. M. Foley, R. H. Gracely, and A. E. Reading, “Pain measurement: an overview,” Pain, vol. 22, no. 1, 1985, pp. 1–31.

C. J. Dunwoody, D. A. Krenzischek, C. Pasero, J. P. Rathmell & R. C. Polomano, “Assessment, Physiological Monitoring, and Consequences of Inadequately Treated Acute Pain,” Pain Management Nursing, vol 9, no 1, pp. 11-21, 2008.

S. Begum, S. Barua & M. Ahmed, “Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and CaseBased Reasoning,” Sensors, vol. 14, no. 7, pp. 11770-11785, 2014.

B. Gupta & K. Jyoti, “Theoretical framework for physiological profiling using sensors and Big Data Analytics,” 2015 Communication, Control and Intelligent Systems (CCIS), 2015.

C. Aydinalp, “A lower power communication protocol for physiological sensors” (Order No. 1601991). Available from ProQuest Dissertations & Theses Global. (1734467190), 2015.

M. V. Villarejo, B.G. Zapirain & A. M. Zorrilla, A. M. A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee. Sensors, vol. 12, no. 12, 6075-610.




How to Cite

Ismail, M. I. S., Ahamed Hassain Malim, N. H., Abd Manaf, A., Mohamed Zain, K. A., & Mohamed Noor, D. A. (2020). Pain Assessment: A Proof of Concept for the Correlation of Sensor-based Physiological Readings to Self-Report Methods. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 12(3), 13–20. Retrieved from