In this paper, we highlight the problem of measuring hand steadiness for the patients with Parkinson's Disease or those who need a rehabilitation program such as brain post-stroke patients. Using the accelerometer, we measure the accelerations against both the body motion and gravity, which is very useful for measuring postural orientations and body movement. In this paper, we present another method for hand steadiness measurement using three-axis accelerometer. A framework named SIERRRA is developed for this purpose to obtain and evaluate the hand reach movements.

In this paper, we introduce a Health signs and Activity recognition MONitoring framework (Hamon). Hamon, of German origin meaning a home protector, is designed to be an enabling prototype for health monitoring applications. As one of the possible applications, we implemented an activity detection prototype using off-the-shelf sensors. The new activity recognition algorithm we present here is based on accelerometers signals, a K-nearest neighbor (KNN) classifier and a Bayesian network.

Biofeedback is an emerging technology being used as a legitimate medical technique for several medical issues such as heart problems, pain, stress, depression, among others. This paper introduces the Multi-Modal Intelligent System for Biofeedback Interactions (MMISBI), an interactive and intelligent biofeedback system using an interactive mirror to facilitate and enhance the user's awareness of various physiological functions using biomedical sensors in real-time.

In this paper, we propose a biofeedback system that employs a Cloud-Oriented Architecture (COA) for the dissemination of biofeedback information and services. The architecture provides the software infrastructure to build biofeedback applications that maintain the user's well-being by monitoring a number of physiological parameters and generate the appropriate feedback. Consequently, the architecture combines the collection of various sensory physiological data and utilizes the existing cloud of resources to provide processing, storage, and responses for biofeedback applications.

Recommender systems are powerful tools that support the user in their quest to find the multimedia they are looking for. Such systems present multimedia contents or provide recommendations by taking into consideration two dimensions of inputs: content (item), and user (consumer). Little attention has been paid to increasing the quality of the experience by understanding the contextual aspect of the user when he/she wants to consume multimedia content.

Given today's mobile and smart devices, and the ability to access different multimedia contents in real-time, it is difficult for users to find the right multimedia content from such a large number of choices. Users also consume diverse multimedia based on many contexts, with different personal preferences and settings. For these reasons, there is a need to reinforce recommendation process with context-adaptive information that can be used to select the right multimedia content and deliver the recommendations in preferred mechanisms.

Ambient intelligence environments are technologically augmented surroundings that aim to provide personalized services to the users based on their context. Identifying these services for the users has become an increasingly challenging task. The overwhelming number of services in the ambient environment has made the selection and management of services even more challenging.

In recent years, users of ambient intelligence environments have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help users in ambient environment get relevant media tailored to their interests, we propose a new method which adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging services. We model the ternary relations among user, resource and tag as a weighted, undirected tripartite graph.