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Cancer patients experience high rates of chronic pain throughout thetreatment process. Assessing pain for this patient population is a vitalcomponent of psychological and functional well-being, as it can cause a rapiddeterioration of quality of life. Existing work in facial pain detection oftenhave deficiencies in labeling or methodology that prevent them from beingclinically relevant. This paper introduces the first chronic cancer paindataset, collected as part of the Intelligent Sight and Sound (ISS) clinicaltrial, guided by clinicians to help ensure that model findings yield clinicallyrelevant results. The data collected to date consists of 29 patients, 509smartphone videos, 189,999 frames, and self-reported affective and activitypain scores adopted from the Brief Pain Inventory (BPI). Using static imagesand multi-modal data to predict self-reported pain levels, early models showsignificant gaps between current methods available to predict pain today, withroom for improvement. Due to the especially sensitive nature of the inherentPersonally Identifiable Information (PII) of facial images, the dataset will bereleased under the guidance and control of the National Institutes of Health(NIH).

Intelligent Sight and Sound: A Chronic Cancer Pain Dataset