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Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or...
This paper presents a method for automatic temporal location and recognition of human actions. The data are obtained from a motion capture system. They are then animated and optical flow vectors are subsequently calculated. The system performs in two phases. The first phase employs nearest neighbor search to locate an action along the temporal axis taking into account both the angle and length of...
The objective of this study is to investigate alternative ways for representing suitably, with the fewest possible assumptions, the information derived from video recordings. It proposes a set of statistical descriptors capable of summarizing all the available information from each video frame. A sequence of such features expresses the object motion implicitly without the need for object detection...
The aim of this study is to determine the best eigenfeatures of four main human postures based on the rules of thumb of Principal Component Analysis namely the KG-rule, Cumulative Variance and the Scree Test followed by statistical analysis. Accordingly, all three rules of thumb suggest the retention of only 35 main principle components or eigenvalues. Next, these eigenfeatures that we named as dasiaeigenposturespsila...
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