Body sensor network (BSN) has played a key role in the rapid advancement of e-Health applications. If it is properly designed, a BSN can act as an ambient intelligent environment by providing us not only time critical human body information but also the context and events mapped with raw sensory data.

Body sensor networks (BSN) can capture physical phenomena from a human body, contextual information from the environment and high level events of a person. Associating contextual information and events with the captured raw sensory data can serve as a crucial input for many applications such as e-health. For example, to accurately and timely monitor an elderly person with several physical disabilities while he is at home or outdoors, the context and event information along with raw sensory data needs to be reached to an e-health service provider to assist in taking time critical decision.

Nowadays, technology advances accelerate the quality and type of services 
provided for health care and especially for monitoring health conditions. Sensors have 
become more powerful to sense different physiological signs and have the ability to be worn 
on the human body using wireless communication modules. A variety of software tools have 
been developed to help in processing a variance list of vital signs by analyzing and 
visualizing data generated by multiple sensors. In this thesis, we introduced a Health ...

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.