Machine Learning to Analyze Facial Imaging, Voice and Spoken Language for the Capture and Classification of Cancer/Tumor Pain

Background: Cancer pain can have a very negative effect on people s daily lives. Researchers want to use machine learning to detect facial expressions and voice signals. They want to help people with cancer by creating a model to measure pain. They want the model to reflect diverse faces and facial expressions. Objective: To find out whether facial recognition technology can be used to classify pain in a diverse set of people with cancer. Also, to find out whether voice recognition technology can be used to assess pain. Eligibility: People ages 12 and older who are undergoing treatment for cancer Design: Participants will be screened with: Cancer history Information about their sex and skin type Information about their access to a smart phone and wireless internet Questions about their cancer pain Participants will have check-ins at the clinic and at home. These will occur over about 3 months. They will have 2-4 check-ins at the clinic. They will check in at home about 3 times per week. During check-ins, participants will answer questions and talk about their cancer pain. They will use a mobile phone or a computer with a camera and microphone to complete a questionnaire. They will record a video of themselves reading a 15-second passage of text and responding to a question. During the clinic check-ins, professional lighting, video equipment, and cameras will be used for the recordings. During remote check-ins, participants will be asked to complete the questionnaire and recordings alone. They should be in a quiet and bright room. The room should have a white wall or background.