A novel feature selection technique for improving wearable activity recognition
نویسندگان
چکیده
Last technological advances in wearable sensors and machine learning are allowing for a new generation of human monitoring techniques, especially devised for the analysis of biomechanics and activity patterns. In this paper, a novel technique to improve the identification of daily physical activity is presented. Taking into account the importance of data featuring and the selection of the most important features for the subsequent pattern recognition stage, a new feature selection methodology based on a filter technique via a couple of two statistical criteria is presented. Satisfactory accuracy rates are achieved by using support vector machines, particularly for preprocessed inertial data from the wrist.
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