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Low-cost sensors and wireless systems can now create a constantly vigilant and pervasive monitoring capability at home, at work, and in conventional point-of-care environments. While progress in this area is underway in sensor technology, mobile computing platforms, and data transport, barriers to large scale application remain ahead, particularly in the area of patient disease diagnosis, which generally requires a diverse set of sensors and instruments that are applied at proper times in response to patient state/behavior. As these sensors may be numerous, and may not be worn comfortably and practicably at all times, a solution is required for the systematic selection of sensors at the point of use. We describe the Incremental Diagnosis Method (IDM) system, an embedded decision support system based on Bayesian statistics and decision analysis theory developed to select or deselect available sensors so that the diagnostic certainty of patient condition best improved while the set of sensors used on the patient body is minimized. IDM has been evaluated in a testbed, the Medical Embedded Device for Individualized Care (MEDIC) system, based on standard, ubiquitous wireless platforms. MEDIC supports local sensing and signal processing, autonomous decision support, and remote reconfiguration and control of wearable components. A detailed evaluation of IDM operation and performance for patient gait analysis is also given in this paper. Finally, we also discuss the many new opportunities provided by IDM and the related future research introduced by this capability.