Fuzzy inference-based fall detection using kinect and body-worn accelerometer

In this paper we present a new approach for reliable fall detection. The fuzzy system consists of two input Mamdani engines and a triggering alert Sugeno engine. The output of the first Mamdani engine is a fuzzy set, which assigns grades of membership to the possible values of dynamic transitions, whereas the output of the second one is another fuzzy set assigning membership grades to possible body poses. Since Mamdani engines perform fuzzy reasoning on disjoint subsets of the linguistic variables, the total number of the fuzzy rules needed for input-output mapping is far smaller. The person pose is determined on the basis of depth maps, whereas the pose transitions are inferred using both depth maps and the accelerations acquired by a body worn inertial sensor. In case of potential fall a thresholdbased algorithm launches the fuzzy system to authenticate the fall event. Using the accelerometric data we determine the moment of the impact, which in turn helps us to calculate the pose transitions. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and reliable low cost detecting of falls.