fall related injuries caused have become a leading cause of fatalities among the elderly. Lots of aging people rely on a cane as a help device to overcome such problems as balance loss and leg weakness, presumed to be at the origin of many fall incidents. In this respect, the internet of things may turn out to be critically helpful, by offering disabled people the assistance and support necessary for achieving a good quality of life and allowing them to participate in the social and economic activities. In this paper, a 3DCane design is depicted, with an implementation for the CANet project, a system whereby a comprehensive monitoring of disabled people can be maintained through the use of their connected walking stick. Actually, the 3Dcane is modeled through consideration of real-time movements for a thorough understanding of the concerned person's state, including fall detection. To note, the 3DCane applies a multi-stage thresholding framework based on an accelerometer. Experiments have revealed that the fall-detection algorithm appears to be robust in term of detection rate and false positive rate.