Abstract
Media content recommendations for a mobile user based on his changing contextual preferences, otherwise called context-aware media recommendations, constitute a very important challenge. Context-aware media recommendation systems take context information such as user preferences, activities, time, location, device, and network capabilities as inputs for media recommendations, whereas the traditional recommendation systems use only user preferences in the form of ratings to deliver media recommendations. This paper presents a generic high-level architecture of context-aware recommendations, discussing its key techniques and solutions, which are based on context acquisition, recognition, and representations, using MPEG-21 and ontology model, and a contextual user profiling process, as well as MPEG-7 for media description model and media presentation adaptation.

Abstract
The unprecedented advancements in broadband and mobile networks, the proliferation and the incredible appeal of smart devices such as smartphones, and the recent emergence of cloud computing are poised to drive the next generation of ubiquitous media delivery and consumption. As more media services become available, mobile users will waste invaluable time, seeking relevant media items. Therefore, to deliver relevant media services, with rich experience to mobile users, media service providers must consider the services that match user's contextual consumption choices. This paper proposes context-aware recommendation techniques to support the delivery of contextually relevant cloud-based media items to mobile users. The recommendation service works with a contextual user profile service, which relates user preferences to contexts in which such preferences are expressed, relying on a context recognition service, which identifies the user's dynamic contextual situation from smartphone built-in sensors. Experimental evaluations, using real world user and online movie data, established that the context-aware recommendation techniques are promising. © 2014 IEEE.

Abstract
The incredible appeals of smartphones and the unprecedented progress in the development of mobile and wireless networks in recent years have enabled ubiquitous availability of myriad media contents. Consequently, it has become problematic for mobile users to find relevant media items. However, context awareness has been proposed as a means to help mobile users find relevant media items anywhere and at any time. The contribution of this paper is the presentation of a context-aware media recommendation framework for smart devices (CAMR). CAMR supports the integration of context sensing, recognition, and inference, using classification algorithms, an ontology-based context model and user preferences to provide contextually relevant media items to smart device users. This paper describes CAMR and its components, and demonstrates its use to develop a context-aware mobile movie recommendation on Android smart devices. Experimental evaluations of the framework, via an experimental context-aware mobile recommendation application, confirm that the framework is effective, and that its power consumption is within acceptable range. © 2014 Springer International Publishing Switzerland.

Abstract
Current solutions for delivering adapted multimedia content in mobile environments take into account only a limited set of contextual information, and can be regarded as passive solutions. We propose a new solution that anticipates user's needs based on the contexts of use and preferences to deliver media content to mobile users. This paper describes the profiling approach of the proposed solution, and a context-aware content-based recommendation for smart devices. Recommendations are issued based on user history, driven by real-time contextual conditions.

Abstract
The incredible appeals of smartphones and the unprecedented progress in the development of mobile and wireless networks in recent years have enabled ubiquitous availability of myriad media contents. Consequently, it has become problematic for mobile users to find relevant media items. However, context awareness has been proposed as a means to help mobile users find relevant media items anywhere and at any time. The contribution of this paper is the presentation of a context-aware media recommendation framework for smart devices (CAMR). CAMR supports the integration of context sensing, recognition, and inference, using classification algorithms, an ontology-based context model and user preferences to provide contextually relevant media items to smart device users. This paper describes CAMR and its components, and demonstrates its use to develop a context-aware mobile movie recommendation on Android smart devices. Experimental evaluations of the framework, via an experimental context-aware mobile recommendation application, confirm that the framework is effective, and that its power consumption is within acceptable range. © 2014 Springer International Publishing Switzerland.

Abstract
Human motion capture systems help clinicians to detect and identify mobility impairments, early stages of pathologies and evaluate the effectiveness of surgical or rehabilitation intervention. Although there is a considerable number of solutions presently available, these systems are often expensive, complex, difficult to wear, and uncomfortable for the patient. With the purpose of solving the formerly mentioned problems, a new wearable locomotion data capture system for gait analysis is being developed. This system will allow the measurement of several locomotion-related parameters in a practical and non-invasive way, also reusable, that can be used by patients from light to severe impairments or disabilities. © 2013 The Textile Machinery Society of Japan.