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Feature selection is a key step for activity classification applications. Feature selection selects the most relevant features and considers how to use each of the selected features in the most suitable format. This paper proposes an efficient feature selection method that organizes multiple subsets of features in a multilayer, rather than utilizing all selected features together as one large feature...
A real-time fall detection system is proposed to distinguish various falls during daily activities. Falls are detected in two steps: first a hierarchical algorithm is used to classify the motion and motionless postures such as lying, sit-tilted, sit-upright, standing and walking; it then analyzes whether the current lying or sit-tilted postures are normal or abnormal, based on posture transition and...
This paper presents details of a convenient and unobtrusive system for monitoring daily activities. A smart phone equipped with an embedded 3D-accelerometer was worn on the belt for the purposes of data recording. Once collected the data was processed to identify 6 activities offline (walking, posture transition, gentle motion, standing, sitting and lying). The processing technique adopted a novel...
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