Co-MEAL: Cost-Optimal Multi-Expert Active Learning Architecture for Mobile Health Monitoring

Mobile health monitoring plays a central role in a variety of health-care applications. Using mobile technology, health-care providers can access clinical information and communicate with subjects in real-time. Due to the sensitive nature of health-care applications, these systems need to process physiological signals highly accurately. However, as mobile devices are employed in dynamic environments, the accuracy of a machine learning model drops whenever a change in configuration of the system occurs. Therefore, data mining and machine learning techniques that specifically address challenges associated with dynamic environments (e.g. different users, signal heterogeneity) are needed. In this paper, using active learning as an organizing principle, we propose a cost-optimal multiple-expert architecture to adapt a machine learning model (e.g. classifier) developed in a given context to a new context or configuration. More specifically, in our architecture, a system's machine learning model learns from experts available to the system (e.g. another mobile device, human annotator) while minimizing the cost of data labeling. Our architecture also exploits collaboration between experts to enrich their knowledge which in turn decreases both cost and uncertainty of data labeling in future steps. We demonstrate the efficacy of the architecture using a publicly available dataset on human activity. We show that the accuracy of activity recognition reaches over 85% by labeling only 15% of unlabeled data. At the same time, the number of queries from human expert is reduced by up to 82%.