Contextual data classification for a ubiquitous intelligent environment

Abstract

Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The K-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample's membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.