A capacitive proximity capture device SensFloor(1) was installed in the HUman at home projecT(2) apartment in Montpellier, South of France. Activations related to participants’ movements were continuously captured and recorded by this smart floor. This low-cost Floor present the issue of spatial precision of collected signals. Actually, the least spatial element is a triangle of 25x50 cm. Moreover, the activation signal is a capacitive one that is not proportional to the weight of the object. As a consequence, it is challenging to organize this space-temporal signals into human behavioral events such as static position, trample, walk, alone or more persons into the apartment and so on. These events will be in the foundation of defining Human@Home metrics. Trajectories were detected, identified and reconstructed by the Walk@Home algorithm(3). In the core of this algorithm is a space-temporal window that scans the raw signals and organize them into a dynamic graph containing the eventual trajectories. Even then, the result is an approximation of the movement of the center of the gravity of a human.