Smartphone Health Biomarkers: Positive Unlabeled Learning of In-the-Wild Contexts

摘要

There has recently been increased interest in context-aware mobile sensing applications due to the ubiquity of sensor-rich smartphones. Our DARPA-funded Warfighter Analytics for Smartphone Healthcare (WASH) project is exploring passive assessment methods using smartphone biomarkers and context-specific tests. Our envisioned context-specific assessments require accurate recognition of specific smartphone user contexts. Existing context datasets were either scripted or in-the-wild. Scripted datasets have accurate context labels but user behaviors are not realistic. In-the-wild datasets have realistic user behaviors but often have wrong or missing labels. We introduce a novel coincident data gathering study design in which data were gathered for the same contexts using both a scripted and in-the-wild study. We then propose positive unlabeled context learning (PUCL), a transductive method to transfer knowledge from highly accurate labels of the scripted dataset to the less accurate in-the-wild dataset. Our PUCL approach for context recognition outperforms state-of-the-art methods with an increase of over 3% in balanced accuracy.