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Falling is a risk factor of vital importance in elderly adults, hence, the ability to detect falls automatically is necessary to minimize the risk of injury. In this work, we develop a fall detection algorithm based in inertial sensors due its scope of activity, portability, and low cost. This algorithm detects the fall across thresholds and residual events after that occurs, for this it filters the acceleration data through three filtering methodologies and by means of the amount of acceleration difference falls from Activities of Daily Living (ADLs). The algorithm is tested in a human activity and fall dataset, showing improves respect to performance compared with algorithms detailed in the literature.