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We address possible solutions for a practical application of Markov Logic Networks to online activity recognition, based on domotic sensors, to be used for monitoring elderly with mild cognitive impairments. Our system has to provide responsive information about user activities throughout the day, so different inference engines are tested. We use an abstraction layer to gather information from commercial...
Many context-aware applications based on activity recognition are currently using mobile phones. Most of this work is done in an offline way. However, there is a shift towards an online approach in recent studies, where activity recognition systems are implemented on mobile phones. Unfortunately, most of these studies lack proper reproducibility, resource consumption analysis, validation, position-independence,...
Rapid population aging and advances in sensing technologies motivate the development of unobtrusive healthcare systems, designed to unobtrusively collect an elderly’s personalized information of daily living and help him actively enjoy a healthy lifestyle. Existing studies towards this goal typically focus on recognition of activities of daily living (ADLs) and abnormal behavior detection. However,...
We propose a method for human activity recognition in videos, based on shape analysis. We define local shape descriptors for interest points on the detected contour of the human action and build an action descriptor using a Bag of Features method. We also use the temporal relation among matching interest points across successive video frames. Further, an SVM is trained on these action descriptors...
In this paper, a novel statistical indoor activity recognition algorithm is introduced. While conditional random fields (CRFs) have prominent properties to this task, no optimal performance is obtained due to the fact that the performance is optimized for offline estimation. Furthermore, no previous researches provide efficient training process to optimize classifiers in on-site recognition perspective...
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