Abstract
A disorder is being recognized as one of the major health issues related to high levels of stress. At the same time, interests about quality of are rapidly increasing. However, diagnosing disorder is not a simple task because patients should undergo polysomnography test, which requires a long time and high cost. To solve this problem, an accelerometer embedded wrist-worn device is being considered as a simple and low cost solution. However, conventional methods determine a state of user to sleep or wake according to whether values of individual section`s accelerometer data exceed a certain threshold or not. As a result, a high miss-classification rate is observed due to user`s intermittent movements while sleeping and tiny movements while awake. In this paper, we propose a novel method that resolves the above problems by employing a dynamic classifier which evaluates a similarity between the neighboring data scores obtained from SVM classifier. A performance of the proposed method is evaluated using 50 data sets and its superiority is verified by achieving 88.9% accuracy, 88.9% sensitivity, and 88.5% specificity.
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