Detection of falling events through windowing and automatic extraction of sets of rules: Preliminary results

Fall detection is very important for the health care especially for elderly people. The automatic discovery of falls in real time with the ability to differentiate them from normal daily activities is crucial. To achieve this aim, this paper proposes an approach based on getting data through a tag placed on the subject's chest, a windowing of the data, the automatic extraction through the DEREx tool of a set of IF-THEN rules able to classify windows as being part of fall or non-fall actions, and a final window composition to assess whether or not each global action was a fall. The approach is then tested on a real-world database containing a set of fall and non-fall actions, and is compared, in terms of classification over windows, against four state-of-the-art machine learning methods. Moreover, its results are also compared, in terms of accuracy in discrimination of the fall actions from the non-fall ones, against those obtained by the database builders through the use of another powerful machine learning algorithm. Numerical results are encouraging, and suggest that the proposed methodology could put solid ground for the design and the implementation of a real-time system for fall detection.