The availability of diverse and powerful sensors embedded in modern Smartphones opportunities for developing context-aware appli human activity data with such devices, data pre required to operate while meeting hardware resource constraints, par present a comparison study for HAR exploiting feature selection approaches to reduce the computation and training time needed for the discrimination of targeted activities publicly available dataset. Results show that Recursive Feature Elimination method combined with Radial Basis Function Support Vector Machine classifier offered the best tradeoff between training time/recognition
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