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Enabling Computational Jewelry for mHealth Applications

Many of the most compelling mHealth applications are designed to enable long-term health monitoring for outpatients with chronic medical conditions, for individuals seeking to change behavior, for physicians seeking to quantify and detect behavioral aberrations for early diagnosis, for home-care providers needing to track movements of elders under their care in order to respond quickly to emergencies, or for athletes monitoring their physiology to improve performance. Developing BAHN applications that require consistent presence and strong security, without depending on a smartphone or without building lots of computation/communication resources into every BAHN device presents a critical challenge for the wide-spread adoption of mHealth technologies. The smartphone is not always with its user [1]: many people set aside their phone while at home or while driving, exercising, or bathing. According to a Pew study, a third of smartphones have been lost or stolen [2]! When the smartphone is not present, the BAHN could lose its foundation; valuable data could be lost, critical events may go unrecognized. Second, smartphones have limited means to authenticate or identify the person holding them; if the phone has been lost or stolen, an app could inappropriately disclose personal health information about the phone’s owner. Third, smartphones are general-purpose devices, not dedicated to health-related applications; it is thus more difficult to evaluate the safety and security of a system when it is sharing resources with other applications.