Identification system for smart homes using footstep sounds

This work proposes an identification system for smart buildings using footstep information. Basically, the system has three modules: pre-processing, feature extraction and selection, and classification of the information. Gait frequency, spectral envelope, cepstral and mel-cepstral analysis and loudness compose the set of parameters used to identify subjects in the smart building. In order to select features, the Fisher´s criterion was used. For the classification task, K-Means and K-NN algorithms were performed along with different methodologies for test and validation. Moreover, the best sets were gathered, increasing the accuracy of classification. Several experiments were performed with 15 subjects wearing 6 different footwear. The classification rates were in the range from 33.5% to 97.5%.