Classification of events from ground sensors - Application to the monitoring of fragile people.

Summary This thesis deals with the detection of events in signals from ground sensors for the monitoring of elderly people. In view of the practical issues, it seems indeed that pressure sensors located on the ground are good candidates for monitoring activities, especially fall detection. As the signals to be processed are complex, sophisticated models should be used. Thus, in order to design a fall detector, we propose an approach based on random forests, while addressing hardware constraints with a variable selection procedure. The performance is improved using a data augmentation method as well as temporal aggregation of the model responses. We then address the issue of confronting our model to the real world, with transfer learning methods that act on the basic model of random forests, i.e. decision trees. These methods are adaptations of previous work and are designed to address the problem of class imbalance, where falling is a rare event. We test them on several datasets, showing encouraging results for the future, and a Python implementation is made available. Finally, motivated by the issue of tracking elderly people while processing a one-dimensional signal for a large area, we propose to distinguish elderly people from younger individuals using a convolutional neural network model and dictionary learning. Since the signals to be processed are mostly steps, the first brick of the model is trained to focus on the steps in the signals, and the second part of the model is trained separately on the final task. This new approach to gait classification allows to efficiently recognize signals from elderly people.