Article

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 hand, using the
volumetric representation features were described in terms of 3D cylindrical
histograms working within a wider range of distances in a faster way and also
guaranteeing good invariance properties. The discrimination capabilities were
evaluated in four different real-home scenarios related with the fields of
ambient assisted living and homecare, namely "dangerous event detection",
"anomalous behaviour detection", "activities recognition" and "natural
human-ambient interaction". For each mentioned scenario, the discrimination
capabilities were evaluated in terms of invariance to viewpoint changes,
representation capabilities and classification performance, achieving promising
results. The two feature representation approaches exhibited complementary
characteristics showing high reliability with classification rates greater than 97%.
CI - Copyright (c) 2013 Elsevier B.V. All rights reserved.

Langue : ANGLAIS