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In this study, importance of user inputs is studied in the context ofpersonalizing human activity recognition models using incremental learning.Inertial sensor data from three body positions are used, and the classificationis based on Learn++ ensemble method. Three different approaches to updatemodels are compared: non-supervised, semi-supervised and supervised.Non-supervised approach relies fully on predicted labels, supervised fully onuser labeled data, and the proposed method for semi-supervised learning, is acombination of these two. In fact, our experiments show that by relying onpredicted labels with high confidence, and asking the user to label onlyuncertain observations (from 12% to 26% of the observations depending on theused base classifier), almost as low error rates can be achieved as by usingsupervised approach. In fact, the difference was less than 2%-units. Moreover,unlike non-supervised approach, semi-supervised approach does not suffer fromdrastic concept drift, and thus, the error rate of the non-supervised approachis over 5%-units higher than using semi-supervised approach.

Importance of user inputs while using incremental learning to personalize human activity recognition models