Sensor-based Measurement of Nociceptive Pain: An Exploratory Study with Healthy SubjectsShow others and affiliations
2022 (English)In: Pervasive Computing Technologies for Healthcare: 15th EAI International Conference, Pervasive Health 2021, Virtual Event, December 6-8, 2021, Proceedings / [ed] Hadas Lewy; Refael Barkan, Springer, 2022, Vol. 431, p. 88-95Conference paper, Published paper (Refereed)
Abstract [en]
Valid assessment of pain is essential in daily clinical practice to enhance the quality of care for the patients and to avoid the risk of addiction to strong analgesics. The aim of this paper is to find a method for objective and quantitative evaluation of pain using multiple physiological markers. Data was obtained from healthy volunteers exposed to thermal and ischemic stimuli. Twelve subjects were recruited and their physiological data including skin conductance, heart rate, and skin temperature were collected via a wrist-worn sensor together with their selfreported pain on a visual analogue scale (VAS). Statistically significant differences (p< 0.01) were found between physiological scores obtained with the wearable sensor before and during the thermal test. Test-retest reliability of sensor-based measures was good during the thermal test with intraclass correlation coefficients ranging from 0.22 to 0.89. These results support the idea that a multi-sensor wearable device can objectively measure physiological reactions in the subjects due to experimentally induced pain, which could be used for daily clinical practice and as an endpoint in clinical studies. Nevertheless, the results indicate a need for further investigation of the method in real-life pain settings.
Place, publisher, year, edition, pages
Springer, 2022. Vol. 431, p. 88-95
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 431
Keywords [en]
pain, sensors, physiological data, healthy subjects
National Category
Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:oru:diva-96422DOI: 10.1007/978-3-030-99194-4_7ISI: 000790610600007Scopus ID: 2-s2.0-85127858196ISBN: 9783030991937 (print)ISBN: 9783030991944 (electronic)OAI: oai:DiVA.org:oru-96422DiVA, id: diva2:1626885
Conference
15th EAI International Conference on Pervasive Computing Technologies for Healthcare (EAI PervasiveHealth 2021), (Virtual conference), December 6-8, 2021
Funder
Vinnova2022-01-122022-01-122022-05-17Bibliographically approved