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This paper describes MediNet, a mobile healthcare system that is being developed to personalize the self-care process for patients with both diabetes and cardiovascular disease. These two diseases were chosen based on their interrelationship. Patients with diabetes are at least twice as likely to have heart disease or a stroke as compared to persons without diabetes. Furthermore, persons with diabetes also tend to develop heart disease or have strokes at an earlier age than other people. MediNet uses a reasoning engine to make recommendations to a patient based on current and previous readings from monitoring devices connected to the patient and on information that is known about the patient. It caters for the uniqueness of each patient by personalizing its recommendations based on individual level characteristics of the patient, as well as on characteristics that groups of patients tend to share.

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This paper takes a critical look at current development efforts with learning objects for Web-based e-learning. It points out the limitations of these efforts and argues that they are still a long way off from realizing the potential of learning objects on the Web. The paper then proposes the notion of objectoriented learning objects to address several of the problems identified.

The Kinect sensor is an attachment for the Xbox gaming console which allows players to interact with games through body movement. This paper explores the possibility of using the Kinect in a clinical setting for detection of Parkinson's, postural, flapping, titubation and voice tremors. All of the physical tremors were simulated and the ability of the Kinect to capture them was evaluated. The Kinect was also used to record voice data from real voice tremors patients. Physical and voice data were gathered from healthy persons for comparison. The results showed that the Kinect could reliably detect voice, postural and Parkinson's tremors. A very consistent graph could be obtained repeatedly for both Parkinson's and postural tremors. For voice tremors there was also a consistent pattern that differentiated a normal voice from one with a tremor. We have therefore proven that the Kinect can consistently record Parkinson's, postural and voice tremors.