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We developed a controllable left ventricle model resembling MR signal characteristics of human myocardium, including pathological conditions, and allowing for the replication of contraction and flow paradigms.

Abstract-The consequence for a fall in elderly people ranges from a soft psychological insecurity and fear of falling to a more physical and severe complication, leading in many cases the victim to death. However, as quick as the subject receives help and medical care, lower is the risk for serious consequences and sequelae. Therefore, several fall detectors have been developed, aiming at detecting fall through wearable devices located on neck, waist, chest, etc. However, for devices located on user wrist, the results are commonly worse, since its detection becomes harder on a body member that moves unimpeded in space. Also, threshold-based methods, whose requires low processing resource and, consequently, becomes easier to be implemented in a wearable embedded device, are not efficient enough to detect falls with high accuracy. This paper presents a threshold based method for fall detection using motion signal variables, evaluating different configurations and their contributions to improve the final fall accuracy, compared to state-of-the-art wrist-based devices.