Human posture recognition with a time-of-flight 3D sensor for in-home applications

A non-invasive system for human posture recognition suitable to be used in several in-home scenarios is proposed and validation results presented. 3D point cloud sequences were acquired by using a time-of-flight sensor in a privacy preserving modality and near real-time processed with a low power embedded PC. To satisfy different application requirements in terms of discrimination capabilities, covered distance range and processing speed, a twofold discrimination approach was investigated in which features were hierarchical arranged from coarse to fine exploiting both topological and volumetric spatial representations. The topological representation encoded the intrinsic topology of the body’s shape in a skeleton-based structure, guarantying invariance to scale, rotations and postural changes, and achieving a high level of detail with a moderate computational cost. In the volumetric representation, on the other hand, postures were described in terms of 3D cylindrical histograms working within a wider range of distances in a faster way and also guarantying good invariance properties. The discrimination capabilities of the approach were evaluated in four different real-home scenarios especially related with ambient assisted living and homecare fields, namely dangerous event detection, anomalous behavior detection, activities recognition, natural human-ambient interaction, and also in terms of invariance to viewpoint changes, representation capabilities and classification performance, achieving promising results. The two approaches exhibited complementary characteristics showing high reliability with classification rates greater than 97% in four application scenarios for which the posture recognition is a fundamental function.

► A novel posture recognition system was presented and validated in four scenarios.
► The usage of TOF vision allowed to overcome well-known passive vision problems.
► TOF images were unable to reveal subject’s identity thus preserving privacy.
► Two discrimination approaches allowed to satisfy complementary requirements.
► Classification rates greater than 97% were achieved in all considered scenarios.