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Abstract

Audiovisual content consumption on mobile platforms is rising exponentially and this trend will continue in the next years as mobile devices become more sophisticated. Thus, smartphones are gradually replacing our desktops as they increasingly become cheaper and more powerful with excellent multimedia processing support. As mobile users go about their routines, they continuously browse the Web, seeking interesting content to consume, and also uploading personal content. However, users encounter huge volume of content, that does not match their preferences, resulting in mobile information overload. Context-aware media personalization (CAMP) was proposed as a solution to this problem. CAMP assists users to select relevant content among alternatives considering users' preferences and contexts. This solution, however, are limited to static contexts. Our contribution is Mobile Context-Aware Media Personalization(MobCAMP), which is a special kind of personalization that utilizes user's contexts and activities to suggest media content according to the user's tastes and contextual situations.

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Is Open Access

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

Andrea Mannini, Angelo Sabatini (2010)

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.