Optimization-based domain adaptation towards person-adaptive classification models

The emergence of inexpensive and unobtrusive physiological sensors has widened their application to newer and innovative areas including proactive health monitoring, smart environments and novel human-computer interfaces. The inherent variability in physiological signals across subjects poses a great challenge to traditional machine learning algorithms which are used to develop generalized classification frameworks. In this paper, we propose an optimization-based domain adaptation (ODA) methodology which can provide reliable classification on a given test subject, using the available data from other subjects. The proposed ODA method selects instances from the source domain (data available from other subjects) based on a novel optimization formulation, to ensure that the selected instances are similar in distribution to the target domain (test subject data) in both marginal and conditional probability distributions. We validated the proposed framework on Surface Electromyogram (SEMG) signals collected from 8 people during a fatigue-causing repetitive gripping activity, to detect different stages of fatigue. Comprehensive experiments on our SEMG data set demonstrated that the proposed method improves the classification accuracy by 19% to 21% over traditional classification models, and by 12% to 18% over existing state-of-the-art domain adaptation methodologies.