Abstract
This article describes a new scheme for a physical activity recognition process based on carried smartphone embedded sensors, such as accelerometer and gyroscope. For this purpose, the WKNN-SVM algorithm has been proposed to predict physical activities such as walking, standing, or sitting. It combines weighted k-nearest neighbours (WKNN) and support vector machines (SVM). The signals generated from the sensors are processed and then reduced using the kernel discriminant analysis (KDA) by selecting the best discriminating components of the data. The authors performed different tests on four public datasets where the participants performed different activities carrying a smartphone. They demonstrated through several experiments that KDA/WKNN-SVM algorithm can improve the overall recognition performances and has a higher recognition rate than the baseline methods using the machine learning and deep learning algorithms.
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