Intelligent Sight and Sound: A Chronic Cancer Facial Pain Dataset

Catherine Ordun, Alexandra Cha, Edward Raff, Byron Gaskin, Alexander Hanson, Mason Rule, Sanjay Purushotham, James Gulley

Keywords: pain detection, facial emotion recognition, multimodal, healthcare, cancer

TL;DR: We introduce a dataset for chronic cancer pain facial detection collected from smartphone videos, collected in an ongoing clinical trial of cancer patients at the National Institute of Health.

Abstract: Cancer patients experience high rates of chronic pain throughout the treatment process. Assessing pain for this patient population is a vital component of psychological and functional well-being, as it can cause a rapid deterioration of quality of life. Existing work in facial pain detection often have deficiencies in labeling or methodology that prevent them from being clinically relevant. This paper introduces the first chronic cancer pain dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical trial, guided by clinicians to help ensure that model findings yield clinically relevant results. The data collected to date consists of 29 patients, 509 smartphone videos, 189,999 frames, and self-reported affective and activity pain scores adopted from the Brief Pain Inventory (BPI). Using static images and multi-modal data to predict self-reported pain levels, early models show significant gaps between current methods available to predict pain today, with room for improvement. Due to the especially sensitive nature of the inherent Personally Identifiable Information (PII) of facial images, the dataset will be released under the guidance and control of the National Institutes of Health (NIH).