A system, methods and computer-readable media are provided for the automatic identification of patients according to near-term risk of sudden kinematic injury (falling). Embodiments of the invention are directed to event prediction, risk stratification, and optimization of the assessment, communication, and decision-making to prevent falling in humans, and in one embodiment take the form of a platform for wearable, mobile, unteathered monitoring devices with embedded decision support. Thus the aim of embodiments of the present invention relates to automatically identifying persons who are at risk for falls through the use of an inexpensive, noninvasive, portable, wearable electronic device and sensors equipped with signal-processing software and statistical predictive algorithms that calculate stability-theoretic measures derived from the digital accelerometer and gyroscope timeseries acquired by the device. The measurements and predictive algorithms embedded within the device provide for unsupervised use in the home or in general acute-care and chronic-care venues and afford a degree of robustness against variations in individual anatomy and sensor placement. In some embodiments, the present invention provides a leading indicator of near-term future abnormalities, proactively alerting the user, for example, 2 hours or more in advance, and providing the wearer and/or care providers with sufficient advance notice to enable effective preventive maneuvers to be undertaken. In one exemplary embodiment, the device is equipped with radiofrequency telecommunication capabilities that enable integration with case-management software, electronic health record decision-support systems, and consumer personal health record systems.
