A Personalized Assessment Platform for Non-invasive Monitoring of Pain

Pain is a health problem of vast dimension that requires prompt diagnosis. However, the evaluation criteria currently used in clinical settings are based on self-rating scales, which are subjective and prone to error. This may cause administering of improper analgesic treatments, either in dosage or in duration. For the scope of providing objective measures of pain and help devising effective therapeutic choices, we designed a personalized platform for sensing and monitoring pain. As input to the platform, visual, speech and physiological clues expressed by the patient during the pain experience are non-invasively measured, then sent to a cloud server to be integrated and analyzed. In this work, preliminary results obtained with the vision-based block of the platform are presented. Because the pain perceived by each individual is highly subjective, once the data are collected from each subject, quantitative descriptors are extracted and personalized selection of relevant features is performed to train a subject-specific regression model. Video sequences of genuine pain felt were tested and weighted accuracy values of 0.81 (0.19) and 0.84 (0.10) were obtained, respectively, for three-class and two-class scenarios, showing benefits and drawbacks of the proposed personalized platform.