Real-time evaluation of an automated computer vision system to monitor pain behavior in older adults.

Abstract

Regular use of standardized observational tools to assess nonverbal pain behaviors results in improved pain care for older adults with severe dementia. While frequent monitoring of pain behaviors in long-term care (LTC) is constrained by resource limitations, computer vision technology has the potential to mitigate these challenges. A computerized algorithm designed to assess pain behavior in older adults with and without dementia was recently developed and validated using video recordings. This study was the first live, real-time evaluation of the algorithm incorporated in an automated system with community-dwelling older adults in a laboratory. Three safely-administered thermal pain tasks were completed while the system automatically processed facial activity. Receiver Operating Characteristic curves were used to determine the sensitivity and specificity of the system in identifying facial pain expressions using gold standard manual coding. The relationship between scoring methods was analyzed and gender differences were explored. Results supported the potential viability of the system for use with older adults. System performance improved when more intense facial pain expressiveness was considered. While average pain scores remained homogenous between genders, system performance was better for women. Findings will be used to further refine the system prior to future field testing in LTC.