A non-invasive technique for posture classification suitable to be used in several in-home scenarios is proposed and preliminary validation results are presented. 3D point cloud sequences were acquired using a single time-of-flight sensor working in a privacy preserving modality and they were processed with a low power embedded PC. In order to satisfy different application requirements (e.g. covered distance range, processing speed and discrimination capabilities), a twofold discrimination approach was investigated in which features were hierarchically arranged from coarse to fine by exploiting both topological and volumetric representations. The topological representation encoded the intrinsic topology of the body's shape using a skeleton-based structure, thus guaranteeing invariance to scale, rotations and postural changes and achieving a high level of detail with a moderate computational cost. On the other …