Surface acoustic arrays to analyze human activities in smart environments

This thesis introduces an approach for tracking three different activities, including their context extension, with a precision of 94% by using multiple pickup/piezo sensors. The mechanical waves, which are created by people touching various objects, can be recognized with these sensors. The combination of classical signal processing and current methods of machine learning enables the implementation of a processing pipeline for classifying these signal events and assigning the propagated event signal to its activity. Compared to the C4.5 CART and BayesNET classifiers, the best precision and performance balance is offered by the SVM classifier. The observed activities in this thesis are Walking, Closing a Cupboard and Falling. Especially Walking and Closing a Cupboard provide a good basis for extending the context. For a context expansion of Walking, the classification classes are split into the shoe types. Closing a Cupboard is divided into the cupboard instances, which have different positions and facing directions, in the environment. To avoid creating a Non class an Impact Filter is applied for preprocessing the recorded signals. The utilized main features are the RMS value and the Zero Crossings of the time-domain signal. They are extended by the FFT vector, statistical values like the mean and standard derivation of this vector as well as the index of the maximal FFT value. With this results, it is possible to lower the issues with common sensors like wearables and cameras. An additional advantage is that it can very easily be integrated in any environment.