This paper presents a multi-feature approach for detection of key postures by using a MESA SR4000 time-offlight 3D sensor managed by a low-power embedded PC. Acquired data were pre-processed by using a well-established framework including self-calibration, segmentation and tracking functionalities. To accommodate different application scenarios, hierarchical coarse-to-fine features were extracted by exploiting two different descriptors: topological and volumetric. The topological descriptor encoded intrinsic topology of body postures in a skeleton-like representation based on geodesic distance. Instead, the volumetric descriptor used a cylindrical voxelization to describe postures in a histogram-based representation. Both synthetic and real datasets were used to evaluate performance. The complementary discrimination capabilities exhibited by the two descriptors allowed to achieve good results in four different application scenarios with a classification rate greater than 96.4%.