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Peer-vasive Computing: Leveraging Peers to Enhance the Accuracy of Self-Reports in Mobile Human Studies

We discuss two methods designed to increase the accuracy of human-labeled data. First, Peer-ceived Momentary Assessment (Peer-MA), a novel data collection method inspired by the concept of Observer Reported Outcomes in clinical care. Second, mQoL-Peer, a platform aiming to equip researchers with tools to assess and maintain the accuracy of the data collected by participants and peers during mobile human studies. We describe the state of the research and specific contributions.